Methods for characterizing a vehicle collision

ABSTRACT

Described herein are examples of a computerized method that comprises: in response to obtaining information regarding a potential collision between a vehicle and an object, obtaining data describing the vehicle for a time period extending before and after a time of the potential collision. The method may determine a likelihood that the potential collision is a non-collision event based on the data describing the vehicle by performing one or more assessments. The assessments may include telematics monitor assessment, driver behavior assessment, road surface feature assessment, trip correlation assessment, and/or context assessment. In response to determining that the likelihood indicates that the potential collision is not a non-collision event, the method may trigger one or more actions responding to the potential collision.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/145,057, filed Feb. 3, 2021, the entire contents of which areincorporated herein by reference.

BACKGROUND

Some organizations, including commercial enterprises or otherorganizations, own and/or operate a fleet of vehicles. For example, acommercial enterprise that provides goods or services to customers atcustomers' homes (e.g., pest control services, or grocery deliveryservices) may own and/or operate a fleet of vehicles used by employeesand/or contractors to travel to the customers' homes.

These organizations often desire to know when one of their vehicles hasbeen involved in a collision, so the organization can respondappropriately and effectively. The organization may want to knowpromptly that a collision has occurred so it can attempt to contact theemployee/contractor who was operating the vehicle and determine whetherthe employee/contractor, or any other person, has been injured and sothat emergency services can be dispatched if needed. The organizationmay also want to know promptly that a collision has occurred so it canpromptly begin investigating the collision and determine whether theorganization is likely to incur any liability for the collision and, ifso, so that it can begin addressing the collision appropriately.

SUMMARY

The present disclosure relates to techniques for characterizing avehicle collision. In an embodiment, the techniques provide acomputerized method comprising: in response to obtaining informationregarding a potential collision between a vehicle and an object:obtaining, for a time period extending before and after a time of thepotential collision, data describing the vehicle during the time period;determining, based at least in part on the data describing the vehicleduring the time period, a likelihood that the potential collision is anon-collision event; and in response to determining that the likelihoodindicates that the potential collision is not a non-collision event,triggering one or more actions responding to the potential collision.

In an embodiment, the techniques provide a system comprising: at leastone processor; and at least one storage medium containing programinstructions that, when executed by the processor, cause the at leastone processor to carry out a method that comprises various operations inresponse to obtaining information regarding a potential collisionbetween a vehicle and an object. The operations in the method include:obtaining, for a time period extending before and after a time of thepotential collision, data describing the vehicle during the time period;determining, based at least in part on the data describing the vehicleduring the time period, a likelihood that the potential collision is anon-collision event; and in response to determining that the likelihoodindicates that the potential collision is not a non-collision event,triggering one or more actions responding to the potential collision.

In an embodiment, the techniques provide a system comprising: at leastone non-transitory computer-readable storage medium containing programinstructions that, when executed by at least one processor, will causethe at least one processor to carry out a method comprising variousoperations. These various operations include: in response to obtaininginformation regarding a potential collision between a vehicle and anobject: obtaining, for a time period extending before and after a timeof the potential collision, data describing the vehicle during the timeperiod; determining, based at least in part on the data describing thevehicle during the time period, a likelihood that the potentialcollision is a non-collision event; and in response to determining thatthe likelihood indicates that the potential collision is not anon-collision event, triggering one or more actions responding to thepotential collision.

The foregoing is a non-limiting summary of the invention, which isdefined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a schematic diagram of an illustrative system with which someembodiments may operate;

FIGS. 2A-2B illustrate flowcharts of example processes that may beimplemented in some embodiments to characterize a collision;

FIG. 3 is a schematic diagram of example operations that may beimplemented in some embodiments to characterize a collision;

FIG. 4 is a schematic diagram of example operations that may beimplemented in some embodiments to characterize a context of acollision;

FIG. 5A is a flowchart of an example process that may be implemented insome embodiments to merge potential collisions;

FIG. 5B illustrates an example of updating a time period followingmerging potential collisions according to some embodiments;

FIG. 6 illustrates a flowchart of an example process that may beimplemented in some embodiments to process multiple potentialcollisions;

FIG. 7 illustrates a flowchart of an example pipeline process that maybe implemented in some embodiments to process harsh acceleration events;

FIGS. 8 and 9 illustrate flowcharts of example rule-based methods thatmay be implemented in some embodiments to process acceleration events;

FIG. 10 is a flowchart of a process that may be implemented in someembodiments to characterize a collision using a trained classifier;

FIGS. 11A-11I illustrate examples of machine learning techniques withwhich some embodiments may operate;

FIG. 12 is a flowchart of a process that may be implemented in someembodiments to train a classifier;

FIGS. 13A-13E show values associated with an illustrative implementationof some techniques described herein; and

FIG. 14 is a block diagram of a computer system with which someembodiments may operate.

DETAILED DESCRIPTION

Described herein are various embodiments of a system that processesinformation describing movement of a vehicle related to a potentialcollision to reliably determine whether a collision occurred and/or todetermine one or more characteristics of the collision.

Different collisions have different effects on the vehicle(s) and peopleinvolved in the collisions and thus may justify different responses. Forexample, a severe collision may justify involving emergency serviceswhile a minor collision may not. A collision in which a first vehicle isstruck by a second from the back may suggest fault lies primarily withthe driver of the second vehicle, and different responses may beappropriate for the owner or operator of the first vehicle than for thesecond vehicle. Detecting a potential collision and reliably determiningwhether the potential collision is likely an actual collision, ordetermining characteristics of a collision, may aid in determining anappropriate response to the potential collision.

Conventionally, accelerometer data has been used to determine whether avehicle has experienced a collision. Accelerometer data is used becauseaccelerometers are simple devices to install and use and also because ofthe relation between acceleration and force, and the conventionalunderstanding that when a vehicle experiences a suddenacceleration/force, this is a sign that the vehicle experienced acollision. In one such conventional approach, accelerometer data isanalyzed to derive an acceleration experienced by the accelerometer (andthus potentially by the vehicle) over time and to determine whether theacceleration at any time exceeds a threshold. If the accelerationexceeds the threshold, the vehicle is inferred to have experienced acollision.

The inventors have recognized and appreciated that such conventionalapproaches were unreliable and limited in the information they were ableto provide. Often, the conventional approach suffered from falsepositives (flagging a collision when there was not a collision) andfalse negatives (not flagging a collision when there was a collision).The inventors have recognized and appreciated that this lack ofreliability is because acceleration alone is not a reliable predictor ofwhether a collision has occurred. Vehicles or accelerometers may, undernormal circumstances, experience accelerations of similar magnitude toaccelerations experienced by a vehicle during a collision when there isno actual collision. For example, an accelerometer unit disposed in apassenger cabin of a vehicle may on occasion be accidentally kicked orbumped, and it is difficult to differentiate an acceleration associatedwith a kick or bump from an acceleration experienced during a collision.In another example, accelerometers on board a vehicle may experienceaccelerations when there is a door slam, when the vehicle is beingloaded (e.g., when objects are being placed in a cargo area of thevehicle), or when the vehicle is being unloaded. In such scenarios, apotential collision may be detected when there is no actual collision.

As another example, an accelerometer unit disposed in a passenger cabinof a vehicle may be loosely mounted and may move while the vehicle isoperating, such as by snapping from one position to another when thevehicle is turning. When the unit moves suddenly, the movement may beincorrectly flagged as a collision. As a further example, a largepothole or other road deformity, or other road surface feature (e.g.,railway track or other road impediments) may, when struck by a vehicle,cause a large acceleration value that may be difficult to differentiatefrom acceleration values associated with collisions. Kicks or bumps, andpotholes, or other road surface features, may therefore be flagged as acollision, a false positive.

The inventors recognized and appreciated that conventional techniqueswere limited in the information they were able to leverage to determinewhether a collision has occurred. Information about a vehicle before orafter a collision is detected has not been used in collision detection,but has instead been used in accident reconstruction, to determine otherevents that may have caused the collision, or events that happened afterthe collision. These techniques do not improve unreliable nature ofacceleration analysis (discussed in the preceding paragraph), forexample, reduce false positive or false negative detection. For example,data obtained subsequent to a collision may be analyzed in aconventional system to determine what happened to the vehicle (e.g., thevehicle has left, the vehicle has blocked the road etc.). These dataobtained after the collision is determined to have occurred, however, isnot in any way related to determining whether the collision occurred andthus does not improve the false positive or false negative detection.

The inventors recognized and appreciated that other telemetry datadescribing a vehicle may be used to enhance the reliability ofprocessing of accelerometer data about a potential collision. Forexample, GPS information, speed information, road condition, vehicleoperating condition (including operating condition of instruments orother components installed in a vehicle), or contextual informationaround the time of the potential collision may all advantageouslyprovide additional information about the potential collision. However,it remains a challenge how these telemetry data may be analyzed toimprove the collision detection accuracy. Some of these data may be usedin a conventional system as a sole decision factor. For example, GPSdata may be used to determine whether a vehicle is off road. If thevehicle is determined to be off road, a determination may be made toindicate that the vehicle experienced an accident. This single-factorapproach often does not account for other scenarios. For example, thevehicle could be parked in a parking lot or an unpaved area; the GPSsensor on the vehicle may be improperly operating; or the GPS sensor maygenerate unreliable data points when an accident occurred. These mayresult in a false positive or false negative decision about a potentialcollision.

The inventors further recognized and appreciated that reliablydetermining whether a collision has occurred and determining informationcharacterizing the collision may be enabled by monitoring movements of avehicle for a longer period of time around a collision. Conventionalapproaches focused on an instant in time, which was inferred to be theinstant of first impact for a collision. The inventors recognized andappreciated, however, that some collisions may last quite some time,such as for more than a second or even up to 10 seconds. Reviewingmovement information for the entirety of the collision may help incharacterizing the collision. Beyond that, though, the inventorsrecognized and appreciated that by reviewing movements of a vehicle froma time starting before a collision and lasting until a time followingthe collision, the reliability of determining whether a collisionoccurred and the reliability of characterizing the collision may beincreased.

The inventors further recognized and appreciated that reliablydetermining whether a collision has occurred and determining informationcharacterizing the collision may depend on reliable data points from atelematics monitor installed in a vehicle, as such telematics monitormay provide information that may be used to determine whether thecollision occurred and/or to characterize the collision. However, oncean accident happens, a telematics monitor may operate improperly. Forexample, a monitor may become loosely installed and may move in a waythat is not directly reflective of movements of the whole vehicles, andthus unreliable acceleration data may be obtained. In another example,acceleration triggers accompanied by an airbag deploying may result inacceleration signals that may be confusable with collisionaccelerations. As another example, collection of GPS signals todetermine location may be less reliable or unavailable after acollision, due to damage to a GPS receiver and/or a location ororientation of a vehicle that limits reception of GPS signals. Thesedifficulties may reduce accuracy of a system in determining whether apotential collision is likely to have been an actual collision.

Accordingly, the inventors have developed new technologies to improvethe reliability of collision detection, for example, by reducing a falsedetection rate. In some embodiments, data describing a vehicle may beanalyzed to identify an indication of a potential collision between thevehicle and at least one other object, such as one or more othervehicles or one or more obstacles. Examples of data describing a vehiclemay include sensor data from various telematics monitors installed onboard a vehicle. For example, the data may include acceleration datafrom an accelerometer, location information from a GPS sensor, enginediagnostics codes recorded on board the vehicle, operational signalsfrom one or more components (e.g., instruments) of the vehicle (e.g.,data or signals output by an airbag controller), data from lasersensors, audio data captured in the vehicle, image/video data from animage capturing device (e.g., a camera) on board the vehicle, and/orother suitable sensor data. Alternatively, and/or additionally, datadescribing the vehicle may be captured from a sensor not on board thevehicle. For example, image data depicting the vehicle may be obtainedfrom a camera installed off of a road the vehicle was traveling.

The data describing the vehicle may include any information about theoperation of the vehicle, the driver of the vehicle, the road thevehicle was traveling, and/or the environment (e.g., street scene) thevehicle was passing by. The data describing the vehicle may be obtainedfrom another device over a communication network, such as from one ormore servers (e.g., a “cloud” system). For example, the data that may beobtained from one or more sensors on board the vehicle may be providedfrom the vehicle to the server(s) or may be downloaded from theserver(s). In another example, the image data captured by a camera ofthe street the vehicle was traveling through may be uploaded to a serverthat processes data describing the vehicle to determine a likelihood ofcollision.

Alternatively, and/or additionally, data describing the vehicle may alsobe generated based, at least in part, on sensor data collected by one ormore sensors of the vehicle or sensors installed elsewhere. For example,the speed of a vehicle may be obtained based on calculations performedon GPS data indicating locations of the vehicle over time, bycalculating from the GPS data the distance traveled over a period oftime. Alternatively, and/or additionally, the speed of a vehicle may beobtained based on one or more sensors or imaging capturing devicesinstalled off of a street the vehicle was passing by. A “pull-over” orstop event, in which a vehicle stops at a given location or “pulls over”to stop not in a road the vehicle was traveling but on a road shoulder,rest area, parking space, or other stopping point adjacent to the road,may be determined from an analysis of speed, acceleration, and/orlocation of the vehicle over time. Various data described above may beobtained from one or more sensors on board the vehicle and uploaded to aserver for processing by, for example, a collision detection facility ofthe server.

In some embodiments, a potential collision at an instant time during avehicle's traveling time may be detected. In some examples, accelerationdata in one or more axes may be used to detect the potential collision.For example, a potential collision may be detected in response to anabrupt increase in acceleration data or otherwise in response to anacceleration value that exceeds a threshold. The threshold may be anabsolute threshold, such as a threshold indicating that the accelerationvalue by itself is above the threshold, or may be a relative threshold,such as that the acceleration value satisfies some condition withrespect to one or more prior acceleration values, such as by being morethan a threshold amount above one or mor prior acceleration values. Inresponse to obtaining information regarding a potential collision, datadescribing the vehicle during a time period before and/or after a timeassociated with the indication of the potential collision may beanalyzed to determine a likelihood that the potential collision is anon-collision event. In some examples, a likelihood may be representedby a numeric value that stands for a probability that the potentialcollision is a non-collision event, e.g., 10%, 20%, 35%, etc. In otherexamples, a likelihood may be a binary indicator, e.g., collision ornon-collision, or 0% and 100%. In other examples, a likelihood may berepresented by multiple levels, e.g., “yes,” “no,” “most likely,”“likely,” “less likely,” or “unlikely” etc. If the likelihood indicatesthat the potential collision is not a non-collision, then the system maytrigger one or more actions responding to the potential collision.

Alternatively, and/or additionally, if the likelihood indicates that apotential collision is not a non-collision, then a classificationprocess may be used to classify the data describing the vehicle, such asusing a trained classifier. Performing such classifying following aseparate determination of the likelihood that the potential collision isa non-collision event may aid in reducing a false detection rate andimprove accuracy and reliability of collision detection and collisioncharacterization. In some examples, based on the result of theclassification, the data describing the vehicle into at least one of aplurality of classes, each class of the plurality of classes beingassociated whether the potential collision is a collision. For example,the data describing the vehicle being classified into a class indicativethat the potential collision is likely to be a collision. In response tothat classification, the one or more actions may be triggered.Additionally, information characterizing the collision may also beobtained from the classification.

In some examples, the analysis of the data describing the vehicle duringthe time period to determine the likelihood that the potential is anon-collision event may be carried out by checking the data against setsof criteria/criterion that are each related to non-collision events. If,in connection with a potential collision, data describing the vehicleduring the time period matches at least one criterion associated with anon-collision event, the system may determine that the potentialcollision was not a collision and instead was the non-collision event.

For example, the data describing the vehicle may be analyzed todetermine whether a telematics monitor that provides at least a portionof the data is improperly operating such that unreliable data areobtained. This may be case if a telematics monitor is improperly mountedin a vehicle, such that the telematics monitor moves during operation ofthe vehicle. If a series of acceleration values reported during the timeperiod regarding a potential collision, or other data describing thevehicle, matches at least one criterion associated with aloosely-mounted telematics monitor, then a potential collision may bedetermined to be associated with a loosely-mounted telematics monitorrather than a collision. Such a criterion may be, for example, a patternof acceleration values that is associated with a movement of atelematics monitor when loosely mounted. Such a criterion may beimplemented as, for example, one or more rules to which the datadescribing the vehicle (e.g., acceleration data or other data) may becompared to determine whether the rule is met. In a case that the ruleis met, the potential collision may be determined to be associated withthe non-collision event (e.g., the loosely-mounted telematics monitor).

As another example that may be implemented in some embodiments, datadescribing the vehicle may be analyzed in connection with road surfacefeatures to determine whether the vehicle is near a road surface feature(e.g., railway tracks or other road impediments) that is prone tocausing acceleration values of the vehicle to be detected asacceleration events. In such case, at least a criterion may be relatedto whether the vehicle traverses through a road surface feature that isprone to causing such acceleration values. The criterion may beimplemented as, for example, one or more rules to which the datadescribing the vehicle may be compared. For example, a location of thevehicle at a time of an acceleration event may be used to determinewhether the location of the vehicle matches a known road surface featureprone to causing the acceleration value of a vehicle travelingtherethrough to be detected as an acceleration event. Examples of amatch of the locations include a scenario when the closest distancebetween the location of the vehicle and the known road surface featureis less than a threshold distance. Additionally, acceleration data ofthe vehicle may be used to determine whether the acceleration datamatches an expected acceleration that may result from a matched roadsurface feature. The determination of the match may be done in a varietyof ways, such as by determining whether a set of acceleration values ofthe vehicle detected over time matches a known pattern of accelerationvalues that may be known to be caused by the road surface feature. Ifthe acceleration data matches the acceleration expected of the roadsurface feature, then the criterion related to the road surface featureis determined to be met. Subsequently, the potential collision may bedetermined to be a non-collision event related to the road featuresurface. This may be explained in that the acceleration event that mayhave resulted in the detection of the potential collision may be causedby the vehicle traversing the road surface feature prone to causing theacceleration event, rather than by a collision.

As another example that may be implemented in some embodiments, at leastone criterion may be related to a driver's behavior around the time ofthe potential collision. The criterion related to driver's behavior maybe implemented, for example, as one or more conditions to which the datadescribing the vehicle around the time of the potential collision may becompared. For example, data describing the vehicle may be analyzed todetermine whether the driver's behavior around the time of the potentialcollision matches any behavior that may match to a non-collision. Insome embodiments, frontal images/videos depicting the driver may beanalyzed to determine whether the driver's head stays in a central areaof the driver seat, and/or whether both of the driver's hands areholding the steering wheel. If one or both of these conditions are met,it may be determined that the potential collision is a non-collision.This may be because, if the potential collision were a collision, it maybe unlikely that the driver's behavior would be unchanged from a“normal” behavior and that the driver's heads and/or hands would stillbe in a same position.

In some embodiments, if the driver's head departs from the central areaof the driver seat, and/or at least one of the driver's hands is off thesteering wheel, it may be determined that the driver experiencedfatigue, or the driver was engaged in some other behavior that may causedistracted driving (e.g., operating a user interface such as texting,radio, navigation system etc.). If it is determined that the driverexperienced fatigue or was distracted, then it may be determined thepotential collision is likely a collision as the behavior of fatigue ordistracted driving may be prone to causing a collision. Alternatively,and/or additionally, if the driver's behavior does not match any of thebehavior prone to causing a collision, then it may be determined thatthe potential collision is likely a non-collision.

As another example that may be implemented in some embodiments, at leastone criterion is related to a trip that is being made during the time oftravel. Data describing the vehicle and/or additional information aboutthe trip may be analyzed by correlating the data describing the vehiclewith the trip information, to determine whether a trip criterion is met.For example, if a detected potential collision is near the beginning ofa trip or near the end of a trip (either in time or distance), it may bedetermined whether a trip criterion related to a likelihood of collisionis met. In response to the determination as to whether a trip criterionis met, it may be determined that the potential collision is likely acollision/non-collision event. In some embodiments, an event near theend of a trip (in time or distance) may likely be a collision, and thus,a trip criterion related to a likelihood of collision may be determinedto have not been met. In some embodiments, an event that occurs at thebeginning of a trip (e.g., soon after the beginning of the trip, in timeor distance) may unlikely be a collision, and thus, the trip criterionmay be determined to be met.

Alternatively, and/or additionally, other events, such as a door slam,when the vehicle is being loaded, and/or when the vehicle is beingunloaded may be detected from the data about the vehicle obtained fromthe various sensors. For example, a door slam may be detected inresponse to readings from one or more sensors installed near the vehicledoors. In another example, a vehicle being loaded/unloaded may bedetected in response to readings from one or more sensors installed nearthe vehicle's trunk lid or hatchback. Once these events are detected, itmay be determined that the potential collision is likely a non-collisionevent.

In some embodiments, at least some of the one or more criteria relatedto non-collision events may each be related to a respectivenon-collision context around a potential collision. Based on theassessment of the one or more criteria each related to a respectivenon-collision context of the potential collision, the system maydetermine whether a potential collision is a non-collision. In someembodiments, metric values (e.g., scores) may be generated for eachcriterion, and the metric values of various criteria may be combined togenerate an overall score, such as a collision score.

In some embodiments, each of the criteria related to a respectivenon-collision context of the potential collision may be implemented as,for example, one or more conditions each describing a scenario aroundthe time and/or location of the potential collision and are related tothe non-collision context. The one or more conditions may each generatea metric value (e.g., a score) indicative of the likelihood of collisionor non-collision, if the condition is satisfied.

In some embodiments, a criterion related to a non-collision context mayinclude one or more conditions related to vehicle “pull-over” or vehiclestop around a detected potential collision. For example, in response todetermining a potential collision, the data describing the vehicle maybe analyzed to assess whether the vehicle experienced a “pull-over” bydetermining whether the vehicle came to a stop within a certain amountof time and distance and/or stopped in an area outside of a travel lanefor vehicles (e.g., side of a road, parking lot, etc.). If the vehiclecame to a stop within the time and distance of an identified potentialcollision, then the likelihood that the potential collision is anon-collision may be low, as the stop may be more likely to beexplainable by a driver stopping a vehicle following a collision. Apull-over score may be calculated based on the number of stops thevehicle experienced within the time and distance, the speed of thevehicle, or the distance of the stop.

In some embodiments, a criterion related to a non-collision context mayinclude one or more conditions related to vehicle speed reduction aroundthe time of a detected potential collision. For example, in response todetermining a potential collision, the data describing the vehicle maybe analyzed to assess whether the vehicle experienced a speed reductionaround the time of the potential collision. In some examples, the speedreduction may include a change of speed of the vehicle. The speedreduction may also include a change of speed ratio relative to the speedof the vehicle. Thus, a speed reduction score may be calculated based onthe change of speed ratio. In such case, a change of speed in alow-speed driving may result in a significant speed reduction scoreindicative of a speed reduction that is likely to cause a collision.

In some embodiments, a criterion related to a non-collision context mayinclude one or more conditions related to vehicle's GPS data qualityaround the time of a detected potential collision. For example, inresponse to determining a potential collision, the data describing thevehicle may be analyzed to determine whether GPS data for the vehiclearound the time of the potential collision is unreliable. One or moreconditions associated with such determination of whether GPS data forthe vehicle is unreliable may include, for example, a sudden drop of anumber of GPS data points collected over time, a change in receptionquality of GPS data over time (e.g., sudden drop in the number ofavailable satellites, such as a drop without a corresponding detectablechange in position), or other change. A low number of GPS points may beindicative of a potential collision. Thus, a GPS score may be calculatedbased on the number of GPS data points before, during, and/or after thepotential collision, or on a comparison of such numbers of GPS datapoints, or on another analysis of GPS data quality over time.

In some embodiments, a criterion related to a non-collision context mayinclude one or more conditions related acceleration context of thevehicle around the time of a detected potential collision. For example,in response to determining a potential collision, the data describingthe vehicle may be analyzed to assess whether an acceleration contextlinked to collision existed around the time of the potential collision.In some examples, the acceleration context may be associated with anumber of impacts that may be generated based on the acceleration dataincluded in the data describing the vehicle. An acceleration contextlinked to collision may be determined based on the accelerationmagnitude in the horizontal plane, the number of impacts within a timeperiod and the speed of the vehicle. Thus, an acceleration context scoremay be calculated based on one or more of the acceleration magnitude,the number of impacts and the movement of the vehicle.

In some embodiments, a criterion related to a non-collision context mayinclude one or more conditions related to road-type context of thevehicle around the time of a detected potential collision. In someexamples, in response to determining a potential collision, the datadescribing the vehicle may be analyzed to determine whether the vehicleexperienced a stop in a travel lane of a road, particular a stop at alocation in the travel lane at which vehicles do not customarily stop.Some type of roads may not typically include vehicle stops in travellanes. For example, vehicles typically do not suddenly stop in a travellane of a highway. A decrease in speed followed by a stop in such atravel lane may be indicative of traffic, but a sudden stop may be moreindicative of a collision. On other roads, such as roads with trafficlights, stop signs, or other traffic control features, cars may oftenstop at certain locations in a travel lane (e.g., at an intersection)but may not stop at other locations (e.g., not at intersections). Assuch, a vehicle experiencing a stop in a travel lane at a location thatis not an intersection, including if the stop was associated with asudden stop, may have been involved in a collision. A vehicle stop scoremay be calculated based on the type of the road the vehicle is travelingand the duration of vehicle stop, for example.

As described above, the various scores of one or more criteria may becombined to determine an overall metric value, e.g., a collision scorethat is indicative of the likelihood that the potential collision isnon-collision event. For example, the values for the one or morecriteria, each related to a respective non-collision event or arespective non-collision context, may each be normalized in the range of[0,1], where “0” indicates unlikely to be an accident and “1” indicateslikely to be an accident. The values from the various criteria may becombined in any suitable manner (e.g., multiplied together, addedtogether, weighted average, or any suitable calculation with weighting,etc.) to generate the overall collision score. If the collision score isbelow a threshold, it may be determined the potential collision islikely a non-collision event. It is also appreciated that values fromdifferent criteria may be each assigned a different weight in thecombination, where the weights of the scores may be obtained through atraining (e.g., machine learning) or empirical data.

In some examples, the one or more conditions associated with a criterionmay each be associated with an individual score depending on whether thecondition is satisfied. In such a process, a score for a respectivecriterion of the one or more criteria may also be generated by combiningthe scores associated the one or more conditions that are associatedwith the respective criterion. In some examples, the scores of one ormore conditions associated with a criterion may be combined (e.g.,multiplied, summed, weighted, etc.) to generate a score for theassociated criterion. For example, the scores of the one or moreconditions may be assigned such that the sum of these scores is in thenormalized range of [0, 1].

The inventors recognized and appreciated that machine learningtechniques may increase reliability of processing of accelerometer datato determine information about a potential collision. Accordingly, inresponse to determining that the potential collision is likely anon-collision event, the data describing the vehicle may be furtheranalyzed, using a trained classifier, to determine whether the potentialcollision is likely to have been an actual collision. As disclosed inthe present disclosure, the data describing the vehicle may be obtainedfrom various telematics monitors during a time period extending beforeand/or after the time of the potential collision.

As discussed above, when analyzing a potential collision, a time periodmay be defined around the time of the potential collision. In somecases, a second potential collision may be detected within that timeperiod. In some embodiments, the two potential collisions may beanalyzed separately. In other embodiments, the second potentialcollision may be determined to be a part of the first potentialcollision, and the two potential collisions may be merged. With such amerge, the time period for the first potential collision may be extendedto be an updated time period from before the first potential collisionto after the second potential collision. With such a merge, for example,a second time period (e.g., of a same length as the first time periodaround the first potential collision) may be defined around the secondpotential collision and a combined time period may be determined thatextends from a start of the first time period to an end of the secondtime period. Subsequent to the merging and determination of theupdated/extended time period, data describing the vehicle may beobtained during the updated time period and used for the analysisdisclosed herein. In a case that a third, fourth, or additionalpotential collisions are determined within the time period, additionalextensions may be made.

While an example has been given of merging in response to detectingsecond or subsequent potential collisions occurring within (e.g., beforethe end of) an already-defined time period for analyzing a potentialcollision, embodiments are not so limited. A different time period maybe defined for merging, in some embodiments. For example, if a secondpotential collision is identified within a threshold time of a firstpotential collision, merging may be performed, even if the secondpotential collision is not within the defined analysis time period forthe first potential collision.

The various embodiments disclosed herein may be implemented in variousconfigurations. In a non-limiting example, a system, such as a collisiondetection facility, may be configured to perform operations fordetecting and outputting a collision score pertaining to a vehicle. Thesystem may detect one or more potential collisions. For example,acceleration data indicating an acceleration value or values thatsatisfy a condition (e.g., a value that exceeds a threshold) may be usedto detect a potential collision. The system may further filter thedetected potential collisions, for example, by removing the falsepositives in the detected potential collision. Various criteria relatedto non-collision events, as described in the present disclosure, may beused to determine a likelihood that a potential collision is anon-collision event. If it is determined that the likelihood indicatesthat the potential collision is a non-collision event, such potentialcollision may be filtered out and thus removed from the list ofpotential collisions. Upon obtaining the updated potential collisions,the system may obtain data describing the vehicle for the updatedpotential collisions.

In some embodiments, for each potential collision of the updatedpotential collisions, a time period may be determined by extending frombefore the potential collision to after the potential collision. Foreach of the potential collisions, the system may determine a collisionscore indicative of the likelihood of that potential collision being acollision. Various embodiments on generating metric values for one ormore criteria related to respective non-collision events are describedin the present disclosure. Thus, the system may generate an overallmetric value, e.g., a collision score. In some examples, the system mayadjust the overall collision score based on whether the vehicletraversed certain road surface feature at the time of the potentialcollision. For example, if, around the time of the potential collision,it is determined that the vehicle was traversing a road surface featureprone to causing acceleration values of vehicles traveling therethroughto be detected as acceleration events, the system may adjust the overallcollision score by lowering the collision score (e.g., setting thecollision score to 0) because the detection of the potential collisionmay likely be caused by the road surface feature the vehicle wastraversing rather than a collision. Then, the system may output thecollision score or display the collision score to a user.

The techniques described herein may provide advantages over conventionalsystems in improving the performance of collision detection, e.g.,reducing the false positive or false negative rate. For example, bydetermining the likelihood that a potential collision is a non-collisionevent, and by using data describing the vehicle in a time periodextending from before and/or after the time the potential collision,more data will be used to reliably decrease the number of falsepositives in collision detection. Further, various data describing thevehicle around the potential collision may be combined in a quantitativemanner, e.g., in the form of a collision score, to determine alikelihood of a potential collision being an actual collision or anon-collision. If the collision score is below a threshold, thepotential collision may be determined to be a non-collision, which canresult in reduced false positives. In such case, the data describing thevehicle or contextual information have been all considered to furtherimprove the performance of collision detection.

In addition, once a determination of the potential collision is analyzedusing the various methods described herein and the likelihood that thepotential collision is a non-collision indicates that the potentialcollision is not a non-collision, a portion or a whole of the datadescribing the vehicle may further be provided to a classifier todetermine whether the potential collision is likely to have been actualcollision. The application of rules in analyzing the data describing thevehicle to determine whether a potential collision is an actualcollision provides advantages in filtering the false positive detectionof potential collision and may remove or reduce the need to have suchpotential collisions analyzed by the classifier. Reducing the number ofnon-collision event analyzed by the classifier may, in turn, improve thereliability of the classifier and accuracy of collision detection and/orclassification. These various techniques, such as the time period thatextends surrounding the potential collision, data combination indetermining whether a potential collision is likely a non-collisionevent, and further using the classifier to determine a likelihood of apotential collision being an actual collision, may improve accuracy ofcollision detection and/or characterization.

Described below are illustrative embodiments of approaches for obtainingand analyzing vehicle information to reliably determine whether avehicle has experienced a collision and/or one or more characteristicsof such a collision. It should be appreciated, however, that theembodiments described below are merely exemplary and that otherembodiments are not limited to operating in accordance with theembodiments described below.

FIG. 1 illustrates a computer system with which some embodiments mayoperate. For example, FIG. 1 includes an organization 100 that mayoperate a fleet of vehicles 102. The organization 100 may be acommercial enterprise, a government or government agency, anot-for-profit organization, or a non-profit organization, or any otherorganization. Embodiments are not limited to operating with anyparticular form of organization, or with formal or informalorganizations. Illustrative examples of such an organization include acommercial service that delivers goods and/or services to customers'homes or businesses, a business that rents vehicles, a municipality thatoperates vehicles within the municipality (e.g., vehicles to performpublic works projects, public safety vehicles like police cars, firetrucks, and ambulances, etc.). The vehicles of the fleet 102 may beoperated by employees and/or contractors of the organization 100, or byothers (e.g., customers of a rental car agency may drive the cars).

The organization 100 may want to be notified promptly when any of thevehicles 102 are involved in a collision. The organization 100 may wishto respond to such a collision by determining whether the driver (e.g.,the employee or contractor) or any other person was injured. Theorganization 100 may also wish to respond to a collision by determiningwhether the vehicle is still safe to operate, or has been damaged to thepoint that it should not be operated and another vehicle should be sentto act in the place of the damaged vehicle (e.g., by taking ondeliveries that the damaged vehicle was to have made, or otherwiseproviding service the damaged vehicle was to be operated to perform).Such information might be inferred or determined from an indication of aseverity of a collision. More severe collisions may be more likely thanless severe collisions to result in injuries or result in vehicles thatcan no longer be safely operated. Accordingly, if severity of acollision could be determined, the organization 100 may also be able toestimate whether anyone was injured or whether the vehicle can still besafely operated.

The organization 100 may also want to know, when a collision hasoccurred, the likelihood that it will incur liability for the collision.Fault for different collisions falls with different parties, and thefault may be inferred from a manner in which two vehicles collided. Theangle at which a vehicle in the fleet 102 struck or was struck byanother object (e.g., another vehicle or obstacle) may thus beindicative of who is at fault for the collision, and may be indicativeof whether the organization 100 will incur liability. For example, if avehicle in the fleet 102 is hit from behind by another vehicle, it maybe less likely that the driver of the vehicle in the fleet 102 is atfault and less likely that the organization 102 will incur liability. Ifthe vehicle in the fleet 102 hits another vehicle with its front end,though, it may be more likely the driver of the vehicle in the fleet 102is at fault and more likely that the organization 102 will incurliability. Accordingly, if angle of impact information can be determinedfor a vehicle involved in a collision, the organization 100 may be moreeffectively able to determine who may be at fault and whether it islikely to incur liability.

FIG. 1 also illustrates a collision between two vehicles 104, 106, inwhich vehicle 104 is being struck from behind by vehicle 106. In thisexample, vehicle 104 is a member of the fleet 102. Techniques describedherein may be used to obtain data describing vehicle 104 (e.g., movementinformation) and/or additional data describing the driver of the vehiclethat may be analyzed to determine whether a collision occurred and tocharacterize the collision, including by determining a severity of thecollision and/or angle of impact on vehicle 104.

In some embodiments, each of the vehicles 104, 106 may be respectivelyequipped with a telematics monitor 104A, 106A. The telematics monitor104A, 106A may include a three-axis accelerometer that indicatesacceleration of the device over time, which may be indicative ofacceleration of the associated vehicle over time. The device 104A, 106Amay be equipped to produce an accelerometer value at a set interval,such as multiple times per second (e.g., 100 times per second), once persecond, or at another suitable interval. In some embodiments, thetelematics monitors 104A, 106A may also be equipped to obtaininformation from one of the associated vehicles. For example, atelematics monitor 104A, 106A may be equipped to connect to an OBD portof an associated vehicle and obtain information from an ECU or OBDsystem of the vehicle. Such information may include fault messagesgenerated by the ECU or OBD system, or messages indicating a state ofcomponents of the vehicle, such as messages indicating whether an airbag has deployed.

A collision detection facility may be implemented as executableinstructions and may analyze information generated or obtained by atelematics monitor 104A, 106A. The collision detection facility mayanalyze the information to determine whether a vehicle associated withthe telematics monitor 104A, 106A has experienced a collision and, ifso, determine one or more characteristics of the collision (e.g.,severity, angle of impact).

In some embodiments, the collision detection facility may be implementedin (e.g., stored by and executed by) the telematics monitor 104A, tomake such determinations about vehicle 104. In other embodiments, thecollision detection facility may be implemented by another device of thevehicle 104, such as a computing device integrated with the vehicle 104(e.g., the ECU, or a computer of the OBD system), or a computing devicedisposed in a passenger cabin of the vehicle 104. Such a computingdevice disposed in the passenger cabin may be a mobile device (e.g.,smart phone, tablet, etc.) or personal computer (e.g., laptop computer),or other suitable device. In other embodiments, the collision detectionfacility may be implemented remote from the vehicle 104. In theembodiment of FIG. 1 , for example, the collision detection facility maybe implemented in one or more servers 108 located remote from thevehicle 104. For example, the server 108 may be one or more serversoperated by a vendor of the telematics monitor 104A, one or more serversoperated by the organization 100, operated by a cloud computingplatform, or other servers.

In still other embodiments, operations of the collision detectionfacility described herein may not be implemented wholly in one locationor another, but may be split in any suitable manner. As one suchexample, operations of a collision detection facility to determinewhether a collision has occurred may be implemented within thetelematics monitor 104A or otherwise local to the vehicle 104, whereasoperations to characterize a collision, once it is determined that acollision is likely to have occurred, may be implemented remote from thevehicle 104 in the server(s) 108.

Regardless of where it is implemented, in accordance with sometechniques described herein, the collision detection facility of theexample of FIG. 1 may detect one or more potential collisions of avehicle based on the data obtained from one or more telematics monitors.For example, the collision detection facility may detect a potentialcollision in response to determining an acceleration event indicative ofan abrupt increase in the vehicle's acceleration. The collisiondetection facility may determine a time period around the detectedpotential collision, for example, extending before and after thepotential collision. The collision detection facility may obtain datadescribing the vehicle during the time period around the potentialcollision. The collision detection facility may further determine alikelihood that the potential collision is a non-collision.

In some examples, a likelihood may be represented by a numeric valuethat stands for a probability that the potential collision is anon-collision event, e.g, 10%, 20%, 35%, etc. In other examples, alikelihood may be a binary indicator, e.g., collision or non-collision,or 0% and 100%. In other examples, a likelihood may be represented bymultiple values, e.g., “collision,” “likely collision,” “most likelycollision,” “likely non-collision,” “most likely non-collision,” or“non-collision.”

If it is determined that the likelihood indicates that the potentialcollision is a non-collision, the collision detection facility maydetermine that there is no actual collision. The facility maysubsequently display or output the result to a user (or, in some cases,may not display or output the result, if the result is that thepotential collision was not a collision or otherwise that there was nocollision).

In some embodiments, if it is determined that the likelihood indicatesthat the potential collision is not a non-collision, the collisiondetection facility may make use of a trained classifier to furtherdetermine whether a collision has occurred and/or to characterize thecollision. The trained classifier may have information associated witheach of the classes with which it is configured, illustrated in FIG. 1as data store 108A. That information may be used by the trainedclassifier to analyze information about the vehicle 104 obtained by thetelematics monitor 104A, including movement data or other data, anddetermine a class that best matches the obtained data.

Each of the classes may be associated with whether or not a collisionhas occurred and/or, if a collision has occurred, one or morecharacteristics associated with the collision. For example, classes maybe associated with a binary decision of whether a collision occurred ordid not occur. As another example, classes may be associated withdifferent levels of likelihood that a collision occurred. As a furtherexample, classes may be additionally or alternatively associated withone or more characteristics of a collision, such as a severity of acollision, different levels of severity of a collision, different anglesof impact, or other characteristics of a collision.

In embodiments in which the collision detection facility is implementedremote from the telematics monitor 104A, the telematics monitor 104A maycommunicate obtained data to the collision detection facility. Thetelematics monitor 104A may include communication components, such asone or more wireless transceivers. The wireless transceiver(s) mayinclude, for example, components for communicating via a Wireless WideArea Network (WWAN), such as via a cellular protocol such as the GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications Service(UMTS), Enhanced Data Rates for GSM Evolution (EDGE), Long-TermEvolution (LTE), or other suitable protocol. In some such embodiments,the telematics monitor 104A may directly communicate with one or morenetworks outside the vehicle 104 to communicate data to the collisiondetection facility. In other embodiments, the telematics monitor 104Amay communicate to such networks via another device disposed local tothe vehicle 104. For example, the vehicle 104 may include communicationcomponents for communicating via a WWAN and the telematics monitor 104Amay communicate to the vehicle 104 to request that data obtained by thetelematics monitor 104A be sent to the collision detection facility. Asan example of such an embodiment, the telematics monitor 104A mayinclude components to communicate via a Controller Area Network (CAN) ofthe vehicle 104 and request that obtained data be transmitted from thevehicle 104. In still other embodiments, the telematics monitor 104A maycommunicate via a mobile device local to the vehicle 104, such as amobile device operated by a driver of the vehicle 104. The mobile devicemay be, for example, a smart phone or tablet computer. In such anembodiment, the telematics monitor 104A may communicate with the mobiledevice via a Wireless Local Area Network (WLAN) or Wireless PersonalArea Network (WPAN), such as any of the IEEE 802.11 protocols or any ofthe Bluetooth® protocols, to request that obtained data be sent to thecollision detection facility.

Together with obtained data describing the vehicle or other information,the telematics monitor 104A may transmit to the collision detectionfacility one or more identifiers for the vehicle 104 and/or for thetelematics monitor 104A, to indicate that the transmitted data relatesto the vehicle 104. Embodiments are not limited to operating with aparticular form of identifier. In some embodiments, a VehicleIdentification Number (VIN), a license plate number, or other identifiermay be used. The collision detection facility may receive thisinformation from a telematics monitor, which may be configured with thisinformation, such as by receiving the information as input when thetelematics monitor is installed in the vehicle 104. The collisiondetection facility may also receive this information when the collisiondetection facility is executing on a computing device integrated withthe vehicle 104 (in which case the facility may obtain the identifierfrom memory), or when the facility receives data from or via the vehicle104, in which case one or more components of the vehicle may add theidentifier to the information that is sent.

In some embodiments, an identifier or contact information for a driverof the vehicle 104 may be obtained and transmitted. For example, a phonenumber that may be used to contact the driver of the vehicle 104 may besent. This may be sent in embodiments in which the collision detectionfacility is executing or, or receives data from or via, a mobile deviceof the driver, in which case the mobile device may send data from thetelematics monitor 104A together with the phone number. In otherembodiments, a driver of the vehicle 104 may “log in” to the telematicsmonitor 104A or otherwise configure the telematics monitor 104A whenfirst operating the vehicle 104, and as part of that configuration mayprovide an identifier and/or phone number for the driver.

In some embodiments, location data for the vehicle 104 may also be sentto the collision detection facility. For example, the telematics monitor104A may include Global Positioning System (GPS) hardware to determine alocation of the telematics monitor 104A, or the telematics monitor 104Amay obtain from vehicle 104 information describing a location of thevehicle 104. The telematics monitor 104 may also transmit this locationinformation to the collision detection facility.

The collision detection facility, upon analyzing the data anddetermining one or more classes that likely describe the potentialcollision, may report the potential collision to the organization 100.For example, the collision detection facility may communicate to one ormore servers 110 associated with the organization 100. The server(s) 100may be associated with a call center or other employee or group ofemployees tasked with reviewing and potentially responding to collisionsor potential collisions. The server 100 may thus be operated by theorganization 100 and/or by a service provider that the organization 100has engaged to monitor and respond to collisions or potentialcollisions.

The collision detection facility may provide various information to theorganization 100 when reporting a collision or potential collision. Forexample, if the collision detection facility determines one or morecharacteristics of the potential collision, such as a severity and/orangle of impact for the collision, the characteristic(s) may be sent tothe organization 100. In some cases, some of the data obtained by thetelematics monitor 104A and sent to the collision detection facility maybe sent. For example, if data was obtained from the vehicle 104, such asinformation indicating whether an air bag was deployed, this informationmay be sent to the organization 100. The identifier for the vehicle 104and/or the telematics monitor 104A may be transmitted, so theorganization 100 can identify a vehicle to which the report relates. Inembodiments in which the collision detection facility receives anidentifier or contact information for a driver, the identifier orcontact information may also be sent. In embodiments in which locationinformation for the vehicle 104 is received by the collision detectionfacility, the location information may also be sent to the organization100.

Upon receipt of a report of a collision or potential collision at theorganization 100, the organization 100 may determine whether and how torespond. The response of the organization 100 may be manual and/orautomatic, as embodiments are not limited in this respect. Inembodiments in which the response of the organization 100 is at leastpartially automatic, the automatic response may be generated using rulesthat evaluate information received from the collision detectionfacility. For example, if a report from a collision detection facilityindicates that the vehicle 104 is likely to have experienced a severecollision, and the report includes location information, thisinformation may satisfy conditions associated with triggering dispatchof emergency services to the location, and the server(s) 110 may triggerthat dispatch without human intervention, such as by sending locationinformation and/or identifying information to the dispatcher. In othercases, though, a person may review the report from the collisiondetection facility and determine how to respond. The person may respondby attempting to contact a driver of vehicle 104, such as using receivedcontact information for the driver, to inquire as to health or safety ofthe driver or others. The person may also contact emergency servicesand/or roadside assistance services in an area in which the vehicle 104is located, to request dispatch of emergency services or roadsideassistance to the vehicle 104 using the location information and/oridentifying or contact information.

Automatically and/or manually carrying out these or other responses may,in some embodiments, include communicating with one or more computingdevices 112 associated with one or more service providers, such asemergency services or roadside services.

Communications in the computer system of FIG. 1 may be carried out usingone or more wireless and/or wired networks, including the Internet,generally depicted in FIG. 1 as communication network(s) 114. It shouldbe appreciated that the communication network(s) may include anysuitable combination of networks operating with any suitablecommunication media, as embodiments are not limited in this respect.

FIG. 1 illustrated examples of components of a computer system withwhich some embodiments may operate. Described below in connection withFIGS. 2-13E are examples of implementations of a collision detectionfacility, including techniques for determining a likelihood that apotential collision is a non-collision event, using a classifier todetermine a likelihood that a potential collision has been an actualcollision, and training a collision detection facility. Theseembodiments may operate with a computer system like the one shown inFIG. 1 , or with another form of computer system.

FIGS. 2A-2B illustrate flowcharts of example processes that may beimplemented in some embodiments to characterize a collision. Theprocesses 200/250 of FIGS. 2A-2B may be implemented in some embodimentsby a collision detection facility. For example, the process 200/250 maybe implemented local to a vehicle (e.g., in a telematics monitor of FIG.1 ) and/or remote from a vehicle. In some embodiments, for example, partof the process 200 of FIG. 2 may be implemented local to a vehicle, suchas operations of blocks 202-208 of the process 200, while another partof the process 200 may be implemented from a vehicle, such as operationsof blocks 210-212.

In some embodiments, in FIG. 2A, process 200 may begin with thecollision detection facility obtaining information regarding a potentialcollision at block 202. In some examples, block 202 may include thecollision detection facility detecting a potential collision at aninstant time during a vehicle's traveling time. Acceleration data in oneor more axes may be used to detect the potential collision. For example,the facility may obtain, e.g., from the vehicle, acceleration dataincluding acceleration values for the vehicle at one or more times andthe facility may detect a potential collision in response to identifyingthat the acceleration value(s) indicate that the vehicle has experiencedat least one acceleration that meets at least one criterion. In someexamples, the at least one criterion may include whether theacceleration of the vehicle at the instant time has a value larger thana threshold, such as 1.0 g, 1.5 g, 2.5 g, or other suitable threshold.Thus, in response to determining that the acceleration of the vehicleexceeded a threshold, the facility may detect a potential collision.

In some embodiments, process 200 continues with the facility determininga time period around the detected potential collision, at block 204. Forexample, the time period may extend before and/or after a timeassociated with the indication of the potential collision. In anon-limiting example, 15 seconds after the potential collision and 15seconds before the potential collision may be used. The process 200 mayfurther include the collision detection facility obtaining datadescribing the vehicle during the time period at block 206. Examples ofdata describing the vehicle are described in the present disclosure, andthe facility may obtain these data from various sources, such as fromtelematics monitors on board the vehicle, or from one or more servers(e.g., the cloud) through a communication network.

Process 200 may also include the collision detection facility analyzingthe data describing the vehicle during the time period of the detectedpotential collision to determine whether the detected potentialcollision may be collision or non-collision, at block 208. In someexamples, the analysis of the data describing the vehicle during thetime period to determine whether the potential collision is anon-collision may be carried out by checking the data against sets ofcriteria/criterion that are each related to non-collision events. If, inconnection with a potential collision, data describing the vehicleduring the time period matches at least one criterion associated with anon-collision event, the system may determine that the potentialcollision was not a collision and instead was the non-collision event.The description of criteria for determining a likelihood that apotential collision is a non-collision event are further described indetail with reference to FIGS. 3-4 .

The collision detection facility may check whether the likelihood fromblock 208 indicates that the potential collision is a non-collision, atblock 210. If the analysis from block 208 indicates that the potentialcollision is not a non-collision event, the process 200 may proceed tothe collision detection facility triggering one or more actionsresponding to the potential collision at block 212. The trigger actionsresponding to the collision may include notifying an operator of a fleetof vehicles of which the vehicle is a member, notifying roadsideassistance, notifying emergency services, attempting to contact a driverof the vehicle, or other actions.

Once the actions are triggered, or, if the likelihood from block 208indicates that the potential collision is a non-collision event, theprocess 200 may end, or continue monitoring in block 202, with eithermonitoring the vehicle that experienced the collision or monitoringother vehicles.

In some embodiments, the likelihood determined from block 208 may be abinary indicator that indicates whether the potential collision is acollision, or is a non-collision event. In other embodiments, the resultfrom block 208 may be a metric, e.g., a value indicating a probabilitythat the potential collision is a collision or non-collision event. Insuch a process, the facility, at block 210, may determine that thelikelihood indicates that the potential collision is likely a collisionif the result from block 208 satisfies one or more criteria, such aswhether the result exceeds a threshold value. In another example, if thelikelihood determined from block 208 is represented by a binary value(e.g., “collision”/“no-collision), then the facility at block 210 maydetermine whether the potential collision is a collision or anon-collision event based on the result from block 208. In otherexamples, the likelihood determined from block 208 may have multiplevalues, e.g., “collision,” “most likely collision,” “likely collision,”“likely non-collision,” “most likely non-collision,” or “non-collision.”

Process 250 depicted in FIG. 2B has similarities to process 200 in FIG.2A, and includes use of a trained classifier to further determinewhether a potential collision is a collision. With references to FIGS.2A and 2B, blocks 252-258 may be performed by a collision detectionfacility in a manner similar to blocks 202-208, respectively. For easeof description, the descriptions of these blocks will not be repeated.

With reference to FIG. 2B, at block 260 the collision detection facilitymay check the result from block 258. If the result from block 258indicates that the potential collision is a non-collision event, thenprocess 250 may proceed to end, or the facility continuing monitoring inblock 252, with either monitoring the vehicle that experienced thecollision or monitoring other vehicles.

If, however, the result from block 258 indicates that the potentialcollision is not a non-collision at block 260, the facility may proceedto block 262 to analyze the data describing the vehicle with a trainedclassifier. Examples of such a trained classifier and the trainingthereof are described in detail in FIGS. 10-13E.

Based on the result of the classification at block 262, the datadescribing the vehicle may be classified into at least one of aplurality of classes, each class of the plurality of classes beingassociated with whether the potential collision is a collision. Forexample, the data describing the vehicle may be classified into a classindicative that the potential collision is likely to be a collision, orthat the potential collision is likely to be a non-collision. Thus, atblock 264, the facility may check the classification result. If theclassification result indicates that the potential collision is likely acollision, then process 250 may proceed to the collision detectionfacility triggering actions responding to the collision at block 266,which may be performed in a manner similar to block 212 (FIG. 2A). Ifthe classification result indicates that the potential collision islikely a non-collision, process 250 may proceed to end, or continuemonitoring in block 252, with either monitoring the vehicle thatexperienced the collision or monitoring other vehicles.

As shown in FIG. 2B, performing such classification at block 262following a separate determination of whether a potential collision maybe a collision or non-collision event at block 258 may aid in reducingfalse positives before providing data to the trained classifier. Thefalse positives may be reduced by filtering out in block 258 suchnon-collision events that could trigger potential false positives, priorto provision of event data to the classifier. Such techniques mayimprove the accuracy and reliability of collision detection andcollision characterization.

FIG. 3 is a schematic diagram of operations that may be implemented insome embodiments to characterize a collision. A collision assessmentoperation of block 300 may be implemented by the collision detectionfacility (FIG. 1 ), including in some embodiments in block 208 of theprocess 200 of FIG. 2A and/or in block 258 of the process 250 of FIG.2B. In some embodiments, the collision assessment operation of block 300may include multiple operations, such as telematics monitor assessmentat block 302, road surface feature assessment operation at block 306,driver behavior assessment operation at block 308, trip correlationassessment operation at block 310, and/or context assessment operationat block 312.

By carrying out one or more of these operations of blocks 302-312, acollision detection facility may analyze data describing the vehicleduring a time period of a detected potential collision and generate aresult that indicates whether the potential collision has beendetermined to be a collision event and/or a non-collision event. Theoperations of analyzing the data describing the vehicle during the timeperiod to determine whether the potential collision is a non-collisionevent may be carried out by checking the data against sets ofcriteria/criterion that are each related to non-collision events. If, inconnection with a potential collision, data describing the vehicleduring the time period matches at least one criterion associated with anon-collision event, the collision detection facility may determine thatthe potential collision was not a collision and instead was anon-collision event.

In some embodiments, the telematics monitor assessment operation atblock 302 may be implemented by the collision detection facility toanalyze the data describing the vehicle to determine whether atelematics monitor that provides at least a portion of the data isimproperly operating such that unreliable data were obtained. This mayinclude a case when a telematics monitor is improperly mounted in avehicle, such that the telematics monitor moves during operation of thevehicle. If a series of acceleration values reported during the timeperiod regarding a potential collision, or other data describing thevehicle, matches at least one criterion associated with aloosely-mounted telematics monitor, then a potential collision may bedetermined to be associated with a loosely-mounted telematics monitorrather than a collision. Such a criterion may be, for example, a patternof acceleration values that is associated with or otherwise produced bya movement of a telematics monitor when loosely mounted. Such acriterion may be implemented as, for example, one or more rules to whichthe data describing the vehicle (e.g., acceleration data or other data)may be compared to determine whether the rule is met. In a case that therule is met, the potential collision may be determined to be associatedwith the non-collision event (e.g., the loosely-mounted telematicsmonitor).

In a non-limiting example, the pattern of acceleration values that isassociated with an improperly operating telematics monitor may includethe number of triggers (e.g., acceleration triggers) the telematicsmonitor has generated during a time length. The inventors haverecognized and appreciated that a defective (e.g., loosely-mounted)accelerometer may often generate too many triggers, accordingly, whetherthe telematics monitor is operating improperly may be determined basedon the number of triggers the telematics monitor has generated duringthe time length. For example, a number of triggers an accelerometer hasgenerated in the past four weeks and/or a daily maximum number oftriggers may be determined. If the daily maximum exceeds a threshold(e.g., 6 triggers or other suitable number of triggers), the telematicmonitor may be determined to be operating improperly. Similarly, if thenumber of triggers in the past four weeks exceeds a threshold (e.g., 70triggers or other suitable number of triggers), the telematic monitormay be determined to be operating improperly.

As another example that may be implemented in some embodiments, the roadsurface feature assessment operation at block 306 may be implemented bya collision detection facility to analyze the data describing thevehicle in connection with road surface features (e.g., railway tracksor other road impediments) to determine whether the vehicle is near aroad surface feature that is prone to causing acceleration values ofvehicles traveling therethrough to be detected as acceleration events.In such case, at least a criterion may be related to whether the vehicletraverses through a road surface feature that is prone to causingacceleration events. The criterion may be implemented as, for example,one or more rules to which the data describing the vehicle may becompared. For example, a location of the vehicle may be used todetermine whether the location of the vehicle matches a known roadsurface feature prone to causing acceleration events. Examples of amatch of the locations include a scenario when the closest distancebetween the location of the vehicle and the known road surface featureis less than a threshold distance.

Additionally, acceleration data of the vehicle may be used by thecollision detection facility to determine whether the acceleration datafor the vehicle during the time period matches an acceleration signalthat would be produced by the vehicle striking the road surface featureat the location of the vehicle at the time of the potential collision.If the acceleration data for the vehicle matches an acceleration signalassociated with the road surface feature at the location of the vehicle,then it may be determined that the vehicle was traversing the roadsurface feature around the time of the potential collision. In suchcase, the criterion related to the road surface feature is determined tobe met. Subsequently, the potential collision may be determined to be anon-collision event related to the road feature surface. This may beexplained in that the acceleration event that may have resulted in thedetection of the potential collision may be caused by the vehicletraversing the road surface feature prone to causing accelerationevents, rather than by an actual collision.

As another example that may be implemented in some embodiments, thedriver behavior assessment operation 308 may be implemented by acollision detection facility to evaluate at least one criterion relatedto a driver's behavior around the time of the potential collision. Thecriterion related to driver's behavior may be implemented, for example,as one or more conditions to which the data describing the vehiclearound the time of the potential collision may be compared. For example,data describing the vehicle may be analyzed to determine whether thedriver's behavior around the time of the potential collision matches anybehavior that matches to a non-collision event. In some embodiments,frontal images/videos depicting the driver may be analyzed to determinewhether the driver's head stays in a central area of the driver seat,and/or whether both of the driver's hands are holding the steeringwheel. If one or both of these conditions are met, it may be determinedthat the potential collision a non-collision.

In some embodiments, if the driver's head departs from the central areaof the driver seat, and/or at least one of the driver's hands is off thesteering wheel, it may be determined that the driver experiencedfatigue, or the driver was engaged in some other behavior that may causedistracted driving (e.g., operating a user interface such as texting,radio, navigation system etc.), or that the driver has moved (or beenmoved) to a different position because of a collision. If it isdetermined that the driver experienced fatigue or was distracted, or hasotherwise moved to a different position, then a likelihood may begenerated to indicate that the potential collision is not anon-collision as the behavior of fatigue or distracted driving may beprone to causing a collision. Or, if the driver has moved to a differentposition after the time of a potential collision, it may be determinedthat the potential collision is not a non-collision because the positionchange may result from a collision. Alternatively, and/or additionally,if the driver's behavior does not match any of the behavior that isprone to causing or may result from a collision, then it may bedetermined that the potential collision is likely a non-collision.

In some embodiments, the time period for obtaining data describing thevehicle and the time period for obtaining data describing the driver maybe different. For example, the time period for obtaining data describingthe vehicle may be 15 seconds before and after a potential collision. Incomparison, the time period for obtaining the driver's behavior may belonger than the time period for obtaining the data describing thevehicle, e.g., 15 minutes before and after a potential collision.

As another example that may be implemented in some embodiments, the tripcorrelation assessment operation of block 310 may be implemented by acollision detection facility to determine at least a criterion relatedto trip being made during the time of travel. Data describing thevehicle and/or additionally information about the trip may be analyzedby correlating the data describing the vehicle with the tripinformation, to determine whether a trip-related criterion is met. Forexample, if a detected potential collision is near the beginning of atrip (either in time or distance) in which the vehicle is making, it maybe determined that a trip criterion related to a likelihood of collisionis not met. In another example, if a detected potential collision isnear the end of a trip (either in time or distance) in which the vehicleis making, it may be determined that the trip criterion is met. Inresponse to the determination as to whether the trip-related criterionis met, the potential collision may be determined to be acollision/non-collision event. In the above example, if a potentialcollision is detected within a time threshold or within a distancethreshold since the beginning of a trip in which the vehicle is making,it may be determined the potential collision is likely a non-collisionevent. Conversely, if a potential collision is detected within a timethreshold or within a distance threshold before the end of the trip inwhich the vehicle is making, it may be determined the potentialcollision is likely a collision event.

In some embodiments, the beginning and end of a trip may be obtainedthrough post-processing of vehicle position data. For example, a timeand/or location of the vehicle may be determined to be the end of a tripwhen the vehicle stops at the location for more than a threshold amountof time such that it appears the driver has reached a destination. Inanother example, subsequent to an extended stop (e.g., a few hours orovernight), a time and/or location of the vehicle may be determined tobe the beginning of a trip when the vehicle starts moving.

As another example that may be implemented in some embodiments, thecontext assessment operation of block 312 may be implemented by acollision detection facility to determine at least some of the one ormore criteria related to collision/non-collision events may each berelated to a respective collision/non-collision context of a potentialcollision. Based on the assessment of the one or more criteria, thesystem may determine whether a potential collision is a non-collision.In some embodiments, a metric (e.g., scores) may be generated by thecollision detection facility for each criterion, and the metric valuesof various criteria may be combined by the collision detection facilityto generate an overall score, such as a collision score. The contextassessment operation is further described in detail in FIG. 4 .

FIG. 4 is a schematic diagram of an illustrative system that may beimplemented in some embodiments to characterize a context of acollision. In some examples, context assessment 400 may be implementedby the collision detection facility in the context assessment operationof block 312 of FIG. 3 . In some embodiments, once a potential collisionhas been identified and data describing the vehicle during a time periodhas been obtained, the collision detection facility may analyze the datadescribing the vehicle to assess one or more conditions to determinewhether one or more criteria related to collision/non-collision contextare met. In some examples, one or more conditions related to contextcriteria may be associated with contextual information of a potentialcollision around the time and/or location of the potential collision.Such conditions may be used by the collision detection facility todetermine a metric value, e.g., a collision score. If the collisionscore is below a threshold, it may be determined that the potentialcollision is likely a non-collision event.

In some examples, context assessment operation of block 400 may include“pull-over” context assessment operation block 402, speed contextassessment operation of block 406, GPS quality context assessmentoperation of block 408, acceleration context assessment operation ofblock 410, or road-type context assessment operation of block 412. Ineach of these assessment operations of blocks 402-412, the collisiondetection facility may analyze the data describing the vehicle in a timeperiod around a potential collision and determine whether one or morecriteria related to the potential collision is met. In some examples, ineach of the context assessment operations 402-412 the facility maygenerate a metric value (e.g., a score) indicative of the likelihood ofcollision or non-collision with respect to a respective contextassessment described above.

In some embodiments, the facility may determine with the “pull-over”context assessment operation of block 402 whether the vehicleexperienced a pull-over around the time of the potential collision. Forexample, in response to determining a potential collision, the datadescribing the vehicle may be analyzed to assess whether the vehicleexperienced a “pull-over” by determining whether the vehicle came to astop within a certain amount of time and distance and/or stopped in anarea outside of a travel lane for vehicles (e.g., side of a road,parking lot, etc.). If the vehicle came to a stop within the time anddistance of an identified potential collision, and stayed at the stopfor more than a threshold period of time, then the likelihood that thepotential collision is a non-collision may be low, as the stop may bemore likely to be explainable by a driver stopping a vehicle following acollision. On the other hand, if the vehicle made several short stopswithin a distance, it is likely that the potential collision is anon-collision event.

In the “pull-over” context assessment operation of block 402, one ormore conditions may be assessed to determine an assessment scoreindicative of the likelihood of the potential collision being acollision. For example, the one or more conditions may include whetherthe vehicle stopped within a threshold time of the time of the potentialcollision and within a threshold distance of the location of the vehicleat the time of the potential collision. In a non-limiting example, Table1 lists examples of scores when different conditions are satisfied. Theparameters used in calculating the score are shown only as examples,whereas various method may be used to obtain the parameters. Forexample, the parameters may be obtained empirically or may be obtainedvia machine learning algorithms.

TABLE 1 Condition Score A “pull-over” event 1.0 is detected and when thevehicle's speed is greater than a threshold (dynamic pullovers) A“pull-over” event 1.0 is detected and when the vehicle's speed is lowerthan a threshold (static pullovers) Double Pullovers 0.5 +0.5*negative_slope_map is detected of the first moving group distancefrom the time of potential collision, mapped between a range of distance(e.g., 500-3000 meters) A short stop followed 0.4 +0.4*negative_slope_map by 1 moving event is of the first moving groupdetected distance from the time of potential collision, mapped between arange of distance (e.g., 500-3000 meters) A short stop followed 0.1 bygreater than one moving events is detected Vehicle started moving 0after potential collision Insufficient data −1

In Table 1, a “pull-over” event may be determined if the vehicle came toa stop within a certain amount of time and distance from the potentialcollision. A detection of one or two “pull-overs” around the time of thepotential collision may be indicative of a likelihood of a collision,thus, the score is set to 1.0. On the other hand, if the vehicle movedafter the potential collision, or moved more than once after a shortstop, the potential collision is likely to be a non-collision, thus, thescore is set to a lower value. If the vehicle had double “pull-overs,”then the score may be determined based on the slope of the map. Doublepullover is when the vehicle pulls over twice. Detection of doublepullovers may capture a scenario where the accident happened in themiddle of the road/intersection and the vehicle was stopped or parkedfor an initial inspection. The vehicle may be asked to pull over againto the side of the road or to the next parking lot nearby for the driverto exchange insurance information with the other party or the police andfinish the investigation. Similarly, if the vehicle had a short stopfollowed by one moving event, the score may be determined in a similarmanner with slightly different weights. A short stop is a stop event fora short period of time after which the vehicle moves on with the rest ofthe trip (e.g., the vehicle stopped at an unusually long stop light ordue to traffic congestion). In some scenarios, the system may determinea moving event if the vehicle has moved exceeding a minimal thresholddistance, and/or a moving time has exceeded a threshold time.

As shown in Table 1, a slope map value may be used to map the context(or any input value) to a score value between 0 and 1. For example, apositive slope map (shown in Table 2) may be used for values thatincrease with the likelihood of an accident increasing, whereas anegative slope map may be used for values that increases with thelikelihood of an accident decreasing (e.g. the lower the speed after theevents, the higher the probability of an accident).

In some embodiments, the slope map value may be obtained from a linearmap. A linear map is a discontinuous function that represents a linearapproximation of a sigmoid function. In some examples, the linear mapmay be used to convert an arbitrary input value to a value between 0and 1. For example, a positive_slope_map (x, min_x, max_x) may bedefined using a linear map as below:

$\begin{matrix}{{{= 0.0},{{if}\mspace{14mu}\left( {x\mspace{14mu}\text{<=}\mspace{14mu}{min\_ x}} \right)}}\mspace{430mu}} \\{{{= 1.0},{{if}\mspace{14mu}\left( {x\mspace{14mu}\text{>=}\mspace{14mu}{max\_ x}} \right)}}\mspace{425mu}} \\{{= {\left( {x - {min\_ x}} \right)\text{/}\left( {{max\_ x} - {min\_ x}} \right)}},{{{if}\mspace{14mu} x} > {{min\_ x}\mspace{14mu}{and}\mspace{14mu} x} < {{max\_ x}.}}}\end{matrix}$Similarly, a negative_slope_map (x, min_x, max_x) may be defined using alinear map as below:

$\begin{matrix}{{{= 1.0},{{if}\mspace{14mu}\left( {x\mspace{14mu}\text{<=}\mspace{14mu}{min\_ x}} \right)}}\mspace{439mu}} \\{{{= 0.0},{{if}\mspace{14mu}\left( {x\mspace{14mu}\text{>=}\mspace{14mu}{max\_ x}} \right)}}\mspace{436mu}} \\{{= {\left( {{max\_ x} - x} \right)\text{/}\left( {{max\_ x} - {min\_ x}} \right)}},{{if}\mspace{14mu}\left( {x > {{min\_ x}\mspace{14mu}{and}\mspace{14mu} x} < {max\_ x}} \right)}}\end{matrix}$

In the examples above, an input value between the lower and upper bounds(e.g., min_x, max_x) is mapped in a linearly increasing and decreasingmanner, for positive and negative slope map, respectively. An inputvalue beyond the lower and upper bounds may be equal to a value at theboundary (0 or 1).

When the negative_slope_map is used in a pullover context, it ishypothesized that the further the distance between the triggered eventsand the pullover location the less likely it is that the pullover eventwas caused by the trigger events. In other words, the further thevehicle drives after the trigger, the less likely it is an accident.Thus, the negative slope map is used to provide a measure of thelikelihood of the event being an accident based on the distance from thetriggered event to the pullover location. The negative slope map may benormalized to provide a maximum context value (or likelihood) of 1 fordistances of smaller or equal to a lower threshold (e.g., 500 m or othersuitable distance) and a minimum context value (or likelihood) of 0 at adistance of greater or equal to a higher threshold (e.g., 3000 m orother suitable distance). Furthermore, the context score is linearlydecreased between these thresholds to provide a linearly decreasingmeasure of likelihood. As an example, if the distance from the triggeredevent to the pullover location is 1000 m, the negative slope map mayreturn a metric of a potential accident of 0.8 (or 80%).

Returning to FIG. 4 , in some embodiments, in the speed contextassessment operation of block 406 the facility may determine whether thevehicle experienced a speed reduction around the time of a potentialcollision. For example, in response to determining a potentialcollision, the data describing the vehicle may be analyzed to assesswhether one or more conditions related to a speed context are satisfied.In some examples, the one or more conditions may be associated with aspeed reduction, which may include a change of speed of the vehicle. Thespeed reduction may also include a change of speed ratio relative to thespeed of the vehicle. In such case, a speed reduction score may becalculated based on the change of speed ratio. A change of speed in alow-speed driving may still result in a significant speed reductionwhich could indicate a likelihood of collision. Similar to otherassessment operations, in the speed context assessment operation 406, anassessment score may be generated based, at least in part, on whetherthe change of speed, the change of speed ratio, and/or the slop of theroad from before to after the potential collision. In a non-limitingexample, Table 2 lists examples of scores when different conditions aresatisfied. The parameters used in calculating the score are shown onlyas examples, whereas various method may be used to obtain theparameters. For example, the parameters may be obtained empirically ormay be obtained via machine learning algorithms.

TABLE 2 Condition Score No Stop event 0 Speed after potential 0collision > speed before potential collision Speed before 0 potentialcollision < T Otherwise 0.6 * positive_slope_map when speed change is inthe range of (10, 70) + 0.4 * positive_slope_map when speed change ratiois in the range of (0.4, 0.8) Insufficient data (e.g., −1  speed beforeor after is unknown)

As will be described with respect to obtaining GPS data points, thespeed change ratio may be determined by the ratio of the speed change(from before to after) relative to the speed before the time of thepotential collision.

In some embodiments, in the GPS quality context assessment operation ofblock 408 the facility may determine whether the GPS data for thevehicle around the time of the potential collision is unreliable. Inresponse to determining a potential collision, the data describing thevehicle may be analyzed to assess whether one or more conditions relatedto GPS data quality are satisfied. For example, in the GPS qualitycontext assessment operation of block 408 the facility may determinewhether the number of GPS points within the GPS data for the vehicleduring the time period has dropped after the potential collision ascompared to the number of GPS points before the potential collision.Additionally, and/or alternatively, a time window in which the availableGPS points are obtained after the potential collision is also comparedto the time window before the potential collision. If a decrease in anumber of GPS data points after the potential collision, and/or thereduction of time window after the potential collision does not satisfycriteria (e.g., is below a threshold), the likelihood of a collision maybe high because these low numbers after the potential collision may haveresulted from an actual accident.

Similar to other assessment operations in FIG. 4 , in the GPS qualitycontext assessment operation of block 408, the collision detectionfacility may generate an assessment score based on a drop of GPS datapoints or a reduction of time window for the GPS data points after thepotential collision. For example, for a detected potential collision, anassessment score may be generated based on a ratio of the number of GPSdata points after the potential collision relative to the number beforethe potential collision. The assessment score may indicate thelikelihood of the potential collision being a collision. In anotherexample, the assessment score may also be generated based on the ratioof the length of window in which GPS data points are available, afterthe potential collision relative to the length of window before thepotential collision. In a non-limiting example, Table 3 lists examplesof scores when different conditions are satisfied. The parameters usedin calculating the score are shown only as examples, whereas variousmethod may be used to obtain the parameters. For example, the parametersmay be obtained empirically or may be obtained via machine learningalgorithms.

TABLE 3 Condition Score Otherwise 0.2 * positive_slope_map(PreTriggerGpsPointCount, 15, 20) + 0.4*positive_slope_map(PostToPreTriggerGpsPointCount, 0.07, 0.2) + 0.4*positive_slope_map(PostToPreTriggerTimeAvailable, 0.05, 0.2)Insufficient −1 data (e.g., no pre- trigger GPS points) Speed < T −1

In some embodiments, in the acceleration context assessment operation ofblock 410 the collision detection facility may determine whether one ormore conditions related to an acceleration context are satisfied. Forexample, one or more conditions may be associated with the accelerationmagnitude in the horizontal plane and the number of impacts thatoccurred over the duration of a data window. Harsh accidents may tend togenerate a higher force than the usual false positive event (e.g.,bumped devices or speed bumps). A dynamic event (e.g., potentialcollision, “pull-over,” false positive event, etc.) refers to when thevehicle is moving greater than a threshold speed, e.g., 5 miles perhour. If the vehicle is moving slower than the threshold speed, theevent may be a static event. In some examples, a dynamic potentialcollision may be a false positive if there was only one significantimpact as opposed to multiple impacts in a real accident. On the otherhand, a trigger that was recorded when the vehicle was stationary, anoisy trigger and often false positive trigger can be filtered out by ahigh number of recorded impacts.

Similar to other assessment operations in FIG. 4 , in the accelerationcontext assessment operation 408, the facility may generate anassessment score based on whether one or more conditions related to anacceleration context are satisfied. For example, the acceleration dataaround a potential collision may be analyzed to determine whether thenumber of impacts from acceleration data for the vehicle during the timeperiod is within a range. The range may depend on whether the vehicle ismoving. In response to determining that the number of impacts from theacceleration data for the vehicle during the time period is within therange, an assessment score may be determined based at least in part onan acceleration magnitude feature, wherein the score is indicative ofwhether the potential collision is likely a collision. The accelerationmagnitude feature may be obtained based on an acceleration magnitude inthe horizontal plane from the acceleration data for the vehicle duringthe time period. In a non-limiting example, Table 4 lists examples ofscores when different conditions are satisfied. The parameters used incalculating the score are shown only as examples, whereas various methodmay be used to obtain the parameters. For example, the parameters may beobtained empirically or may be obtained via machine learning algorithms.

Condition Score Vehicle is stationary and 0 NumberOfImpacts > T1 Vehicleis stationary and accel_mag_feature NumberOfImpacts <= T1 Potentialcollision is dynamic and 0 NumberOfImpacts >= T2 Potential collision isdynamic and 0.5 * accel_mag_feature + 0.5 * NumberOfImpacts > T3 and <T2 num_of_impact_feature Other conditions accel_mag_feature

The accel_mag_feature may be obtained based on acceleration magnitude inthe horizontal plane, and num_of_impact_feature may be obtained based ondetected number of acceleration impacts that occur over a time durationaround the potential collision. The thresholds T1-T3 may be learnedthrough a training process or may be empirical data. For example, T1 maybe set to 4, T2 may be set to 20. In other words, when the vehicle is instationary, if the number of impacts triggered from the accelerationdata is below a low threshold, e.g., T1, or above a high threshold,e.g., T2, the potential collision is unlikely to be a collision. Whenthe vehicle is moving, and the number of impacts is above a highthreshold, e.g., T2, then the potential collision is unlikely to be acollision. During other situations, the assessment score may be based onthe accel_mag_feature and/or num_of_impact_feature.

In some embodiments, the collision detection facility may determine inthe road-type context assessment operation of block 412 whether thevehicle experienced a stop around the time of the potential in a travellane at which vehicles do not customarily stop. In some examples, inresponse to determining a potential collision, the data describing thevehicle may be analyzed to determine whether the vehicle experienced astop in a travel lane of a road, particular a stop at a location in thetravel lane at which vehicles do not customarily stop. Some types ofroads may not typically include vehicle stops in travel lanes. Forexample, vehicles typically do not suddenly stop in a travel lane of ahighway. A decrease in speed followed by a stop in such a travel lanemay be indicative of traffic, but a sudden stop may be more indicativeof a collision. On other roads, such as roads with traffic lights, stopsigns, or other traffic control features, cars may often stop at certainlocations in a travel lane (e.g., at an intersection) but may not stopat other locations (e.g., not at intersections). As such, a vehicleexperiencing a stop in a travel lane at a location that is not anintersection, including if the stop was associated with a sudden stop,may have been involved in a collision. A vehicle stop score may becalculated based on the type of the road the vehicle is traveling andthe duration of vehicle stop, for example.

The one or more conditions may be associated with a location of a stopof the vehicle around the time of the potential collision, and/or a timeduration of the stop of the vehicle. For example, the type of the travellane of the road on which the vehicle was traveling may be determinedbased on the location of the vehicle stop. If it is determined that atime duration of the stop of the vehicle is above a duration threshold,it may be determined that the vehicle experienced a stop in a travellane at which vehicles do not customarily stop. The duration thresholdmay be dependent on the type of the road.

Similar to other assessment operations in FIG. 4 , in the road-typecontext assessment operation of block 408, the collision detectionfacility may generate an assessment score based on whether one or moreconditions related to the road-type context are satisfied. In anon-limiting example, Table 5 lists examples of scores when differentconditions are satisfied. The parameters used in calculating the scoreare shown only as examples, whereas various method may be used to obtainthe parameters. For example, the parameters may be obtained empiricallyor may be obtained via machine learning algorithms.

TABLE 5 Condition Score the road-type of the lane the vehicle is 1traveling matches a highway road-type, the location of the potentialcollision lies within an intersection or is on one of the major roadtypes, and a dynamic pullover or double pullover is detected a dynamicpullover or double pullover is 0.8 detected, and the road-type isnon-major roads there is a short stop event around the 0.7 potentialcollision on major roads there was insufficient data or the vehicle 0.6was stationary before and after the potential collision Other conditions0

Under these various conditions, the road-type of the lane in which thevehicle is traveling, whether or not the vehicle is moving, whetherthere is a “pull-over” event, and/or the duration of a stop areconsidered for a matched road type. For example, under the firstcondition in Table 5, if the location of the potential collision lieswithin an intersection or is on one of the major road types, and thepull over label is either dynamic pullover or double pullover, then ascore of 1.0 may be assigned, indicating that the potential collision islikely a collision. The reasoning is that these types of pulloversusually do not occur within major road types for a long period of time.

Under the second condition in Table 5, a lower score but high value maybe assigned (e.g., 0.8 or other suitable values) if the facility detectsa dynamic pull over or double pullover and the road-type is a non-majorroad. The reasoning is that “pull-overs” may be more common onresidential or track road types. Similarly, under the third condition inTable 5, a short stop event around the potential collision on majorroads may indicate a likelihood of collision. Thus, a lower score (e.g.,0.7 or other suitable values) may be assigned. Under the fourthcondition, a lower score (e.g., 0.6 or other suitable values) may beassigned if there was insufficient data or the vehicle was stationarybefore and after the potential collision. Such event may indicate alesser, but still a likelihood of collision. Thus, a lower score (e.g.,0.6 or other suitable values) may be assigned. For all other events, ascore of 0 was assigned, indicating that collision may be unlikely.

The collision detection facility may combine the metric values ofvarious context assessment operations 402-412 to generate an overallscore. For example, the values from each context assessment operation(e.g., 402-412) may each be normalized in the range of [0,1], where “0”indicates that an accident is unlikely and “1” indicates that anaccident is likely. The values from the various context assessmentoperations (e.g., 402-412) may be multiplied together or otherwisemathematically combined (e.g., added, etc.) to generate the overallcollision score. The context assessment scores from 402-412 may be eachassigned a weight in the combination.

As discussed above, a time period may be defined around the time of thepotential collision, and data describing the vehicle during the timeperiod may be analyzed to determine a likelihood that the potentialcollision is a non-collision. In some cases, a second potentialcollision may be detected within that time period, where the secondpotential collision may be determined to be a part of the firstpotential collision, and the two potential collisions may be merged.With such a merge, the time period for the first potential collision maybe extended to be an updated time period from before the first potentialcollision to after the second potential collision, and subsequentlyadditional data (in an extended time period) may be obtained andanalyzed to determine whether the potential collision is likely anon-collision. The merging operations of multiple potential collisionsare described in detail with reference to FIGS. 5A-5B.

FIG. 5A is a flowchart of an example process that may be implemented insome embodiments for a collision detection facility to merge potentialcollisions. FIG. 5B illustrates an example of updating a time periodfollowing a merge if potential collisions according to some embodiments.FIGS. 5A-5B may be implemented in various systems and processesdescribed in FIGS. 1-4 , for example, by the collision detectionfacility (FIG. 1 ) in connection with block 204 of FIG. 2A, or block 254of FIG. 2B.

In some embodiments, the process 500 may include determining a firstpotential collision at block 504. For example, the first potentialcollision may be detected using various embodiments examples describedabove, including in connection with FIGS. 1, 2A-2B.

Optionally, in process 500 the collision detection facility may alsodetect multiple potential collisions. For example, the process 500 maybegin with the collision detection facility determining accelerationmagnitudes from accelerometer data at block 502 along a time line. Atblock 504, the collision detection facility may further detect one ormore potential collisions, in response to determining that thehorizontal acceleration magnitudes exceed a threshold at one or moreinstances of time. Among the detected potential collisions, thecollision detection facility may determine a first potential collisionat block 506 and check other potential collisions within a time periodfrom the first potential collision at block 508. For example, additionalpotential collisions are checked within a threshold time (e.g., 20seconds, or other suitable time period) of the first potentialcollision. Once all of the potential collisions within that thresholdtime of the first potential collision are determined, these potentialcollisions are merged into one potential collision at block 516.Consequently, the collision detection facility updates the time periodat block 518.

A non-limiting example is further illustrated with reference to FIG. 5B.A first potential collision is detected at time T1. Within a time periodof T1, a second potential collision is detected at T2. The two potentialcollisions are then merged, and the time period for the first potentialcollision is further updated to include an extended time period, whichextends from before the time of the first potential collision and afterthe time of the second potential collision.

Other variations of the merging operation may be possible. For example,instead of searching for other potential collisions with a time periodof the first potential collision, once a second potential collision isfound in the time period of the first potential collision, the timeperiod is instantly updated and extended to extend after the secondpotential collision. Subsequently, other potential collisions aresearched in the updated time window. The operations of merging potentialcollisions and updating the time period may be repeated until no otherpotential collisions are found in the updated time period.

Once potential collisions are merged and the time period is updated,data describing the vehicle and/or additional data describing the drivermay be obtained during the updated time period in place of obtainingdata during the original, shorter time period. The collision detectionfacility may then determine whether the potential collision(s) arecollisions, based on the data describing the vehicle that is collectedacross the extended time period.

As previously described, data describing the vehicle may be obtainedduring the time period of a potential collision. This may be implementedby the facility, in some embodiments, to store various tables containingdata describing the vehicle, where the data are obtained from varioustelematics monitors or generated from the readings of the telematicsmonitors. Some data are generated along with the detection of potentialcollisions (e.g., based on acceleration values as previously described),whereas some data are generated using the data previously generated.

In a non-limiting example, once the potential collisions are detected, aHarshTriggers Table is generated for each potential collision. Forexample, for each potential collision (triggers), the facility maygenerate latitude/longitude/speed by using triggers with raw GPS datawith a window (e.g., a 10 second window, 5 seconds before and after thetrigger datetime when a respective potential collision is detected). Thefacility may calculate the speed by taking an average of all the GPSpoints. The facility may calculate latitude/longitude by taking thelatitude/longitude reading from the closest GPS point to the triggerdatatime of the detected potential collision, e.g., TriggerDateTime. Thefacility may also generate a unique ID for the detected potentialcollision, e.g., “TriggerId.”

In some embodiments, the facility may further generate two downstreamtables related to the detected potential collisions (triggers). Thesedownstream tables may include the vehicle dynamics data of the harshacceleration event within the trigger window with respect to the eventsin the HarshTriggers table. For example, the facility may generate atrigger window for GPS data, e.g., HarshTriggerWindowsGps table bytaking all triggers from the HarshTriggers table where length of windowless than 60 or number of datapoints in a trigger window is less than 60(other window length or number may also be possible). The facility mayuse interpolated data to get corresponding GPS data, speed, bearing, andignition data based on DateTime and HardwareId, to generate the triggerwindow for GPS data.

In some embodiments, the facility may also generate another downstreamtrigger window for acceleration data, e.g.,HarshTriggerWindowsAcceleration by taking all triggers fromHarshTriggers table where length of window less than 60 or number ofdatapoints in a trigger window is less than 60 (other window length ornumber may also be possible). The facility may use interpolatedacceleration data to get corresponding GPS, speed, X, Y, Z, bearing andignition data based on DateTime and HardwareId, to generate the triggerwindow for acceleration data. The interpolated data, such asinterpolated GPS data is provided in an interpolated dataset in case theraw GPS data does not include any data points within the trigger window.

In some embodiments, the facility may further generate GPS windowfeatures and Acceleration window features based on theHarshTriggerWindowsGps table and HarshTriggerWindowsAcceleration table.For example, the GPS window features are calculated using the upstreamHarshTriggerGpsWindow. This contains the following basic statistics ofthe window: Min, Max, Avg, Std_dev, and percentiles for speed. Further,the facility may generate the follow columns for three differentscenarios (total window, before trigger, after trigger):

ActualWindowLength—The actual GPS window length is seconds which isdifferent from the queried length because of infrequent GPS logging;

QueriedWindowLength—The queried window length of GPS points by our queryin seconds;

Sampling rate for the above two which states the amount of samples thatare obtained per second;

NumberOfValidPoints—number of GPS points obtained in the window whichhave GPSValid=1; and

NumberOfPoints—total number of GPS points obtained in the window.

The facility may further generate SpeedBeforeWeightedAvg, which is aweighted average using the GPS points that fall between a range of time(e.g., 1 to 11 seconds) before the first trigger datetime (first pointwhere an acceleration event is detected). For example, for a GPS pointthat is before the first trigger datetime t, a window time the extendsfrom t-Δt/2 to t+Δt/2 is used.

The facility may further generate SpeedAfterWeightedAvg, which is aweighted average calculated using the GPS points that fall between arange of time (e.g., 1 to 11 seconds) after the first trigger datetime(first point where an acceleration event is detected)

Based on the speed, the facility may further generate SpeedChange,SpeedChangeRatio (ratio of speed before to speed after to determine howmuch the speed was reduced by), StopEvent (0, if there was no GPS pointwith Speed<5 in the gps window), PostTriggerTimeToStopEvent (time ittook to get a stop event post trigger), PostTriggerDistanceToStopEvent(distance it took to come to a stop event), and QualityLabel.

In some embodiments, this GPS Window features table is used downstreamin an operation, such as block 406 (FIG. 4 ) to calculate a collisionscore related to speed context.

In some embodiments, the acceleration window features may be calculatedusing the acceleration data windows provided in theHarshTriggerAccelerationWindow table. This features table contains thefollowing basic statistics and metrics of the event data windows, suchas NumberOflmpacts which is based on the impacts extracted from thehorizontal acceleration magnitude; MaxAccelMag which is the maximumacceleration magnitude in the horizontal plane; Min, max, ave, std_devof all acceleration components; and general data quality metrics thatare generated for three different scenarios (total data window, pointsbefore trigger, and points after trigger). These general data qualitymetrics may include, for example:

ActualWindowLength—The actual acceleration data window length in secondswhich is different from the queried length because of infrequentacceleration data logging;

QueriedWindowLength—The queried data window length of acceleration datapoints by our query in seconds;

Sampling rate for the above two which states the amount of samples areobtained per second;

NumberOfPoints—total number of acceleration data points obtained in thewindow; and

ClosestPoint—time difference between trigger in the adjacentacceleration data point (only for before and after scenario).

The facility may further generate QualityLabel which is based on thecalculated metrics above, and ActiveWindowLengthAroundTrigger which isthe actual data window length of a filtered acceleration signal thatremoves low activity acceleration readings before and after the trigger.

The listed acceleration features are used in the downstream query toperform operation of acceleration context assessment (e.g., block 410 inFIG. 4 ).

In obtaining data describing the vehicle, the facility may furthergenerate a trigger window for fault code data for each of the potentialcollisions. In some embodiments, a downstream table(CorrelateTriggersAndEngineFaults) may be created by using the reducedharsh triggers table with fault code events that fall within a window(e.g., 60 seconds before or after the trigger time, or other windowtime). This may be implemented by taking all triggers from HarshTriggerstable and using the fault codes of the vehicle to get all the correlatedfault code events. Examples of fault code may include whether an airbagwas deployed or identify the location of impact (frontal, rear, left,and right). The criteria for a fault code to be correlated is that itmust fall within a window 60 seconds (other window time may also bepossible) before or after a trigger event. The facility may further usethe correlated fault codes with other comparison dataset to get thefault description which identifies the type of collision detectionfault.

In some embodiments, the fault code data may include engine fault codeof the vehicle, and may be obtained from the vehicle's Engine ControlUnit (ECU) and/or otherwise available from or via the vehicle's On-BoardDiagnostics (OBD) facility (which, as used herein, also refers to anOBD-II system). For example, information indicating multiple engineerrors at the same time may be a strong indicator that a collisionoccurred. Using this as a strong sign that a collision occurred,movement information (e.g., accelerometer and/or speed data) might beanalyzed characterize the collision, such as the severity of thecollision and/or a direction of impact. As another example, if any of avehicle's sensors indicated a fault at a time of a potential collision,this may be a sign of damage associated with a collision and indicativethat the potential collision was a collision. Movement information maythen be analyzed to characterize the collision.

The various data, which describe the vehicle, or any other data that maybe used in detecting the potential collisions or determining alikelihood that the potential is a non-collision, may be stored in anysuitable format, such as being implemented as metadata, or as datalabels. Variations of the embodiments described in FIGS. 2-5B may beimplemented by the collision detection facility, with examples shown inFIGS. 6-9 .

FIG. 6 illustrates a flowchart of an example process that may beimplemented in some embodiments for a collision detection facility toprocess multiple potential collisions. The example process 600 may beimplemented as a system in the collision detection facility (FIG. 1 ) inconnection with various embodiments described in FIGS. 2A-5B, forexample. In some embodiments, process 600 may start with the collisiondetection facility detecting one or more potential collisions at block602. For example, acceleration data indicating an abrupt increase inacceleration may be used to detect a potential collision.

Optionally, the facility may merge potential collisions and generate anupdated time period for the merged potential collision. The descriptionof merging potential collisions and updating the time period aredisclosed in the present disclosure, such as in FIGS. 5A-5B.

The process may further include the facility filtering the detectedpotential collisions to generate updated potential collisions at block604 by removing the potential collisions that are false positives (ornon-collision events). In some examples, removing the false positivesmay include determining a likelihood that a potential collision is anon-collision event, using various operations described in the presentdisclosure. If it is determined that the likelihood indicates that thepotential collision is a non-collision event, such potential collisionmay be filtered out, and thus, removed from the list of potentialcollisions.

For example, removing noisy triggers may include removing those triggerscaused by an improperly operating telematics monitor as described in thepresent disclosure, such as 302 (FIG. 3 ). Removing the false positivesmay also include determining “pull-over” events, the operations of whichare described in the present disclosure, such as block 402 (FIG. 4 ).Removing the false positives may also include trip correlationassessment operation, which is described in the present disclosure, suchas block 310 (FIG. 3 ).

With further reference to FIG. 6 , process 600 may include the collisiondetection facility obtaining data describing the vehicle for updatedpotential collisions at block 606. In some embodiments, block 606 mayinclude the facility determining a time period for each of the updatedpotential collisions and obtaining data describing the vehicle in thetime period of a respective potential collision. The determination ofthe time period for a potential collision is described in the presentdisclosure, such as block 204 of FIG. 2A or block 254 of FIG. 2B. Forexample, for each potential collision, a time period may be determinedby extending from before the potential collision to after the potentialcollision.

With further reference to operation at block 606, in the example processin FIG. 6 , in process 600 the collision detection facility may obtainor further generate data describing the vehicle around each of thepotential collisions. For example, in process 600 the facility maygenerate data for the filtered triggers, including, for example, speed,latitude, and longitude that are extracted from the originalHarshTriggers table. The facility may also generate RoadType (which canbe used for road-type context assessment operation (e.g., block 412 inFIG. 4 ), for example. RoadType may be generated by using the triggersthat have latitude/longitude and spatial reference data, with thecriteria being that the road segment must lie within a thresholddistance (e.g., 15 meters, or other values) of the trigger point. Then,the triggers may be matched with the closest road such that there is atmost one road type matched per trigger. The road type categorization maybe used by following OSM guidelines as there are some variations ofusage based on country.

In some embodiments, in process 600 the facility may further generatedata including TriggerType determined by the speed (which may bedynamic, static, or unknown). For example, if the speed is null, thenthe TriggerType may be unknown. This may be possible, for example, whenno GPS points are available in a threshold window time (e.g., 10seconds, or other values). If the speed is less than a low thresholdspeed (e.g., 5 mph, or other values), then it is deemed as a statictrigger. Otherwise, it is a dynamic trigger.

In process 600 the facility may further generate TripLabel,PulloverLabel. In some embodiments, the facility may also generateEngineFaultLabel by looking at the correlated engine faults anddetermining whether or not an airbag has deployed or nearly deployed andhow many impact based faults were observed. Once this is determined,data labels may be generated to indicate “Airbag deployed+impact” or“Airbag deployed+multiple impacts” or “Airbag deployed”. If there is nomatch, the label is classified as unknown.

For each of the potential collisions, the facility may further determinea collision score indicative of the likelihood of that potentialcollision being a collision at block 610. Various embodiments ongenerating metric values for one or more criteria related to respectivenon-collision events are described in the present disclosure, such as inFIGS. 1-4 . Thus, the facility may generate an overall metric value,e.g., a collision score at block 610.

As a variation of the various embodiments described in the presentdisclosure, road type feature assessment operation of block of 306 ofFIG. 3 may be used to further adjust the collision score instead ofbeing used to filter out non-collision events from detected potentialcollision events. For example, at block 612, the facility may determinewhether the result from block 610 indicates that the vehicle is nearcertain road surface feature at the time of the potential collision.Operations of determining road surface feature are described in thepresent disclosure, such as block 306 of FIG. 3 . For example, in block612 the facility may check whether a potential collision happens nearloading bays, railway tracks, expansion joints on the highway etc.,where these areas are prone to causing acceleration values of vehiclestraveling therethrough to be detected as acceleration events. If it isdetermined that one or more conditions related to the road surfacefeature assessment are met, the facility may determine that the vehiclewas traveling through the road surface feature. Accordingly, thefacility may adjust the collision score at block 614. For example, thefacility may lower the collision score (e.g., setting the collisionscore to 0) because the detection of the potential collision may likelybe caused by the road surface feature the vehicle was traversing ratherthan a collision. Then, the facility may output the collision score ordisplay the collision score to a user at block 616. The facility mayrepeat operations at blocks 610-616 for each of the potential collisionsthrough operation at block 618, until all of the potential collisionsare processed.

FIG. 7 illustrates a flowchart of an example pipeline process that maybe implemented in some embodiments to process “harsh” accelerationevents, which may be events with a high acceleration that may be apotential collision. In some embodiments, a pipeline process 700 may beimplemented in various systems or processes described in the presentdisclosure, such as by the collision detection facility (FIG. 1 ) inconnection with processes 200 (FIG. 2A), 250 (FIG. 2B), 300 (FIG. 3 ),400 (FIG. 4 ), 500 (FIG. 5A), 600 (FIG. 6 ), and/or any sub-blocksthereof. The collision detection facility may reduce the amount ofpotential collision events from a collision detection point of view andto provide a metric of a potential collision for the filtered downevents.

In the example of FIG. 7 , the filtering is achieved in multiple stages.For example, the trigger events table in Filter Level 0 includes allevents triggered by an acceleration threshold which are then reduceddown to the harsh triggers table in Filter Level 1 by excludingexcessively logging devices. As such, the false positive triggers arereduced. Only after the first reduction of triggers are the detaileddriving data extracted and analyzed. Operations of excluding excessivelylogging devices are described in the present disclosure, such as inblock 302 of FIG. 3 . The harsh triggers table may contain informationthat is then used in the second filtering stage and to generate apotential collision score as shown in the flow chart. The result may bean even more filtered down events table (as shown in Filter Level 2)appended with accident metrics and metadata and a collision score. As aresult, the two-stage process as shown in FIG. 7 improves efficiency ofthe pipeline process and the accuracy of the system.

FIGS. 8 and 9 illustrate flowcharts of example rule-based methods thatmay be implemented in some embodiments to process acceleration events.With reference to FIGS. 8 and 9 , process 800 and 900 each may beimplemented in various systems or processes described in the presentdisclosure, such as by the collision detection facility (FIG. 1 ) inconnection with processes 200 (FIG. 2A), 250 (FIG. 2B), 300 (FIG. 3 ),400 (FIG. 4 ), 500 (FIG. 5A), 600 (FIG. 6 ), or any sub-blocks thereof.

In processes 800 and 900, the collision detection facility may detectpotential collisions based on a respective acceleration trigger,validate the detected potential collisions, for example, by analyzingdata describing the vehicle against one or more criteria related tonon-collision events. With both processes 800 and 900, the facility maygenerate a metric value by assessing various context, such asacceleration context, “pull-over” context, speed context, and/orlocation/road-type context. The output metrics value may indicate thelikelihood of each potential collision being an actual collision.

Various blocks in processes 800 and 900 may be implemented in anysuitable system or process described in the present disclosure. Forexample, blocks 802/902 may be implemented in the collision detectionfacility of FIG. 1 , such as in connection with block 202 of FIG. 2A,block 252 of FIG. 2B, block 504 of FIG. 5 , or block 602 of FIG. 6 .Blocks 804/904 of processes 800/900 may be implemented, for example, bya collision detection facility (FIG. 1 ) in connection with block 208 ofFIG. 2A, block 258 of FIG. 2B, block 300 of FIG. 3 , block 400 of FIG. 4, or block 604 of FIG. 6 . Blocks 806/906 of processes 800/900 may beimplemented, for example, by a collision detection facility (FIG. 1 ) inconnection with block 208 of FIG. 2A, block 258 of FIG. 2B, block 300 ofFIG. 3 , block 400 of FIG. 4 , or block 610 of FIG. 6 . Varioussub-blocks in 806/906 may also be implemented in, for example, blocks410, 402, 406, or 412 of FIG. 4 . Processes 800 and 900 may be performedin a similar manner, except that in process 800 (FIG. 8 ), 2.5 g may beused as a threshold to detect potential collisions. In process 900 (FIG.9 ), 1.5 g may be used instead as a threshold to detect potentialcollisions. Thus, process 900 may detect more potential collisionsrelated to driving in lower speed. As such, one or more conditionsassociated with various context-related criteria, such as accelerationcontext, “pull-over” context, speed context and location/road-typecontext may vary.

Variations of the embodiments described in FIGS. 2-5B may be implementedby the collision detection facility. For example, in some embodimentspreviously described, data describing the vehicle during a time periodof a potential collision may be analyzed to determine a likelihood thatthe potential collision is a non-collision. If it is determined that thelikelihood indicates that the potential collision is not anon-collision, the data describing the vehicle may further be providedto a classifier to determine the likelihood that the potential collisionhas been an actual collision. Alternatively, the data describing thevehicle during the time period of the potential collision may beprovided to the classifier without first determining the likelihood thatthe potential collision is a non-collision. Such variation, as well asthe classifier that may used in various embodiments, are described infurther detail with reference to FIGS. 10-13E.

FIG. 10 illustrates an example of a process that may be implemented by acollision detection facility in some embodiments. The process of FIG. 10may be implemented local to a vehicle (e.g., in a monitoring device ofFIG. 1 ) and/or remote from a vehicle. In some embodiments, for example,part of the process 1000 of FIG. 2 may be implemented local to avehicle, such as operations of blocks 1002-1006 of the process 1000,while another part of the process 1000 may be implemented from avehicle, such as operations of blocks 1008-1012.

The process 1000 begins in block 1002, in which a collision detectionfacility obtaining information regarding a potential collision.

In some embodiments, the collision detection facility may obtaininformation regarding a potential collision by monitoring over time amagnitude of total acceleration experienced by an accelerometer of avehicle and/or of a monitoring device. The total acceleration may be avalue derived from acceleration detected by the accelerometer indifferent axes. For example, in a case that the accelerometer is athree-axis accelerometer, the total acceleration may be derived fromcomputation performed on acceleration experienced in each of the threeaxes. The three axes may be forward-backward, right-left, and up-down insome cases. In some embodiments, the magnitude of total acceleration maybe calculated as the square root of the sum of the squares of theacceleration in different direction. For example, if the acceleration inthe forward-backward direction is assigned to “x”, the acceleration inthe right-left direction to “y,” and the acceleration in the up-downdirection to “z,” the magnitude of the total acceleration may be:acc _(total)=√{square root over (x ² +y ² +z ²)}The magnitude of the total acceleration is a scalar value. This valuemay be used in block 1002 as part of obtaining information on whetherthe vehicle has experienced an event that may (or may not be) acollision—a potential collision. It is appreciated that other variationsof the acceleration magnitude may also be used. For example, a magnitudeof horizontal acceleration (in the x direction) may be used.

For example, in some embodiments, if the vehicle experiences a totalacceleration at a time that is above a threshold, this may be taken as asign of a potential collision that is to be further evaluated todetermine whether it is a collision or is not a collision. As should beappreciated from the foregoing, acceleration alone is seldom a reliableindicator of a collision, as other events may also be associated withhigh accelerations, such as a kick or bump of a monitoring device or thevehicle striking a pothole or other road deformity. Accordingly, themagnitude of total acceleration is not taken as a sign of a collision,but rather used as a sign of a potential collision that is to beinvestigated further.

Thus, in block 1002 in some embodiments, the collision detectionfacility determines the total acceleration over time, such as at a timeinterval. That time interval may be, for example, multiple times persecond (e.g., 100 times per second), once per second, or other suitableinterval, then determines whether the total acceleration at any timeexceeds a threshold. If not, the collision detection process ends. Insome embodiments, the collision detection facility may return to block1002 and continue monitoring the total acceleration over time, orotherwise obtaining information on a potential collision.

It should be appreciated, however, that embodiments are not limited tousing a threshold to determine whether a potential collision hasoccurred, as embodiments may obtain information regarding a potentialcollision in other ways.

For example, in some other embodiments, an indication of a potentialcollision may be obtained by identifying a total acceleration that is alargest in a time period. When a magnitude of total acceleration at atime exceeds the magnitude of total acceleration of other times, such asother times within a time window surrounding a time being analyzed, thathigher total acceleration may be taken as an indication of a potentialcollision. This may be the case even if the magnitude of acceleration atthat time is lower than the magnitude at other times. In such a case,the collision detection facility may use a sliding time window to, overtime, analyze acceleration data within the time window to determinemagnitude of total acceleration at times within the time window and todetermine the highest magnitude in the window. The time of that highestmagnitude may then be taken as a time of a potential collision and takenin block 1002 as information regarding a potential collision.

In some other embodiments, rather than the collision detection facilityusing a sliding time window to identify a maximum total accelerationwithin the time window, a sliding time window may be used thatdetermines every successive point in time to be an indication of apotential collision. At each time step, a next acceleration sample maybe taken as an indication of a potential collision, and taken in block1002 as information regarding a potential collision.

Once the collision detection facility obtains information regarding apotential collision in block 1002, then in block 1004 the collisiondetection facility defines a time period that spans a time before andafter the time at which the total acceleration exceeded the threshold.The time may be long enough to last before and after a collision, if thetime of the potential collision is at the beginning, during, or at theend of a collision. For example, if collisions are determined to last atleast three seconds, the time period may be 6 seconds long: threeseconds before the time of the potential collision, and three secondsafter. If collisions are determined to last at least 5 seconds, the timeperiod may be 10 seconds. The inventors recognized and appreciated thatsome collisions may last up to 10 seconds, so a time period of 20seconds may be advantageous. It should be appreciated, though, thatembodiments are not limited to being implemented with any particulartime period. Further, while in some embodiments the time period may besymmetrically defined around the time from block 1004, in otherembodiments the time period may be asymmetrically defined.

In block 1006, the collision detection facility obtains data describingthe vehicle during the time period defined in block 1004. The data thatis obtained may be movement data describing movements of the vehicle inthe time period. The data describing the movements may be accelerationdata indicating an acceleration of the vehicle in three axes atintervals (e.g., the same interval that may be used in block 1002 toobtain acceleration data) during the time period. In some embodiments,the acceleration data may also be processed to determine additionalinformation describing movements of the vehicle in the time period. Forexample, for each set of acceleration data for each interval, amagnitude of total acceleration may be determined, in the same mannerthat may have been used, in some embodiments, in block 1002. As anotherexample, speed of the vehicle at the interval may be determined. In someembodiments, speed information may be determined from calculationsperformed on accelerations over time, potentially together with locationdata.

In some embodiments, in addition to or as an alternative to accelerationinformation, other data describing the vehicle may be obtained. Forexample, the collision detection facility may obtain data from avehicle, such as from an ECU or OBD system of the vehicle. The obtainedinformation may include, for example, speed at each of the times forwhich acceleration data was obtained. The obtained information mayadditionally or alternatively include messages generated by one or morecomponents of the vehicle, such as information indicating a state of thecomponent(s). The state information may include, for example, whetherany of the components of the vehicle have generated a fault messageand/or have changed a state, such as, for an air bag system, whether anair bag deployed. The data that is obtained from the vehicle may beinformation for the time period defined in block 1004.

In block 1008, the information that is obtained in block 1006 isanalyzed with a trained classifier of the collision detection facility.As discussed above in connection with FIG. 1 , the trained classifiermay include multiple different classes that are associated with vehicledata for different scenarios, where the scenarios include whether acollision occurred and/or characteristics of collisions (e.g., severity,angle of impact, etc.).

Each class may be associated with data describing combinations of data(e.g., movement data or other data) that are associated with thescenario described by the class. For example, if a class is associatedwith a collision having occurred and with a collision that is a severecollision in which the vehicle was rear-ended by another, the class maybe associated with characteristics of movement data and/or other datathat define such a severe rear-end collision. This information maydefine the class and be used to determine whether new data (for apotential collision to be analyzed) fits any of the classes.

Each class may be defined by different movement data because each typeof collision may be associated with different movements, which allowsfor differentiating collisions, and because normal vehicle operations(with no collision) may also be associated with movements that differfrom movements associated with collisions, which allows fordifferentiating collisions from normal driving. For example, astraightforward rear-end collision may include movements that areprimarily forward-backward movements. A collision in which the vehicleis struck broadside by another vehicle may, on the other hand, beassociated with right-left movement data. If data is input for apotential collision, and that data includes primarily forward-backwardmovements and includes very little right-left movement, it may be morelikely that the potential collision is a rear-end collision than thatthe potential collision is a broadside collision. Severe collisions mayalso demonstrate different movements than not-severe collisions, andnot-collisions may demonstrate different movements than collisions. Acomparison of data for a potential collision to data describingdifferent classes of collisions may therefore allow for determiningwhether a collision occurred and/or for determining one or morecharacteristics of a collision.

Accordingly, when the data obtained by monitoring device 104A isanalyzed with the trained classifier, the movement data and/or otherdata may be compared to each of the classes defined by the trainedclassifier. The collision detection facility may generate a probabilityindicating a level of match between each of one or more classes and thedata. The probability for a class indicates a likelihood that theinformation associated with that class is an accurate description of thedata. For example, for the example class above that is associated with acollision having occurred that is a severe rear-end collision, the inputdata may be compared to determine whether it matches the data for thatclass. If the collision actually was a severe rear-end collision, theinput data may appear similar to the data for the class and a highprobability of match may be generated. If, however, the input data isassociated with a not-severe front-end collision, there may not be ahigh degree of match to the severe rear-end collision class and,accordingly, a low probability of match may be generated by thecollision detection facility. In such a case, though, the trainedclassifier may have another class for a not-severe front-end collision,and there may be a high probability of match to that class. As a result,the collision detection facility may generate a probability of matchbetween the input data and each of one or more classes maintained by thetrained classifier.

These probabilities may be used to determine whether a collisionoccurred and/or characteristics of the collision. For example, a classthat has a highest probability may be selected as the accurate answer,and the collision information for that class (whether a collisionoccurred and/or characteristics of such a collision) may be chosen asthe likely correct descriptor of the collision. As another example, theprobabilities may be compared to a threshold to determine whether any ofthe probabilities for any of the classes are above the threshold. If so,all of the classes for which a probability are above a threshold may bereported as potential matches for the potential collision, for a user toreview each of the potential matches and the collision information foreach of the potential matches. As another example, the probabilities maybe compared to determine whether one or more of the probabilities differfrom others by more than a threshold amount, such that one or more couldbe determined to be potential correct matches whereas others are, ascompared to those one or more, less likely to be correct. Those one ormore that stand out from the others may then be reported as potentialmatches for the potential collision, for a user to review each of thepotential matches and the collision information for each of thepotential matches. Embodiments are not limited to any particular mannerof analyzing probabilities and selecting one or more potential correctmatches from the probabilities.

Accordingly, by comparing the data obtained in block 1006 to the datadefining the different classes/scenarios, the trained classifier candetermine which of the classes is a likely match or best match for thedata obtained in block 1006. The collision detection facility maytherefore, in block 1008, determine one or more classes that are alikely or best match to the data obtained in block 1006.

In block 1010, based on the class(es) determined in block 1008, thecollision detection facility determines whether the potential collisionfrom block 1002 is likely to be or have been a collision. This mayinclude determining whether the class that best matches the dataobtained in block 1006 is a class associated with a collision, orwhether any of the classes to which the obtained data is a good match isa class associated with a collision. This may alternatively includedetermining whether a probability of match to any class associated witha collision exceeds a threshold, or whether a probability of match toany class associated with no collision exceeds a threshold.

If it is determined in block 1010 that a collision is not likely, thenin the embodiment of FIG. 10 the collision detection process may end, orreturn to block 1002 and continue monitoring acceleration over time orotherwise obtaining information regarding another potential collision.

If, however, the collision detection facility determines in block 1010that a collision is likely to have occurred, then in block 1012 thecollision detection facility triggers actions responding to thecollision. This may include notifying an operator of a fleet of vehiclesof which the vehicle is a member, notifying roadside assistance,notifying emergency services, attempting to contact a driver of thevehicle, or other actions discussed above. Once the actions aretriggered, the collision detection facility may end the process 1000, orcontinue monitoring in block 1002, with either monitoring the vehiclethat experienced the collision or monitoring other vehicles.

In some embodiments, the collision detection facility may evaluate aclass identified as the most likely match for a potential collision forwhich data was received and analyzed by the collision detectionfacility. If the best match determined by the classifier indicates thata collision is unlikely to have occurred, the collision detectionfacility may not report the potential collision to the organization 100.If, however, the collision detection facility determines that acollision may have occurred, the facility may report the potentialcollision to the organization 100. In other embodiments, however, thecollision detection facility may report to the organization 100 everypotential collision it analyzes, but may report the potential collisionto the organization 100 together with a value indicating a probabilitythat the potential collision was a collision. A person at theorganization 100 (or a vendor for the organization 100) reviewing thereport may then analyze the likelihood that the potential collision wasa collision and, based on the probability, determine whether and how torespond.

In one example described above of the implementation of block 1002, thetime period is defined in block 1004 to be a time period surrounding atime at which a total acceleration exceeded a threshold, which is oneexample of a way in which information regarding a potential collisionmay be obtained. In some embodiments, the time at which a totalacceleration exceeds a threshold may trigger an analysis of a timeperiod before and after that time, to identify a maximum totalacceleration in the time period. This time period may be the same ordifferent than the length of the time period of block 1004. In some suchembodiments, once the maximum total acceleration in the time period isdetermined, the time period of block 1004 is defined based on a timeassociated with that maximum total acceleration, and data is obtained inblock 1006 for that time period.

The collision detection facility was described in connection with theexamples of FIGS. 1, 2B and 10 as implementing machined learningtechniques, using a trained classifier. It should be appreciated thatembodiments are not limited to implementing the trained classifier orthe machine learning techniques in any particular manner.

In some embodiments, the machine learning may be implemented using ak-Nearest Neighbor technique. k-Nearest Neighbor (short k-NN) is anexample of instance-based learning. This means that the training data isbeing stored for comparison purposes. New data will be classified bytaking a defined number of the closest training data into consideration.The k-NN algorithm is explained in the following example shown in FIG.11A. The goal is to determine a classification of the “New” data pointby considering how similar it is to its neighbors in the graph. The“New” data point is positioned in the graph at coordinates determined byone or more data values associated with the “New” data point. The “k” inthe k-Nearest Neighbor algorithm refers to how many other data pointsare chosen for evaluation. Points are chosen for having a closest lineardistance to the “New” data point in the graph. FIG. 11A shows twodifferent options, one with k=3 and one with k=6. When three neighborsare considered, it is seen that two of the three are class “A” whileonly one is class “B,” and thus the “New” data point will be determinedto be a member of class “A.” On the other hand, in the example where sixneighbors are considered, only two of the six are class “A” while theother four are class “B.” As such, for the k=6 example, class “B” willbe chosen for the “New” data point. k-NN is a good choice for a trainedclassifier for a small data set with few dimensions, because of its highreliability and computational simplicity in these situations.

In other embodiments, a Random Forest (RF) technique may be used. RFbelongs to the machine learning category called “decision trees” and canbe applied to classification tasks. Well-advanced trees which are thefoundation of the RF. An RF model is trained by creating a decision treethat can represent most of the training data, by creating paths throughthe tree to labels reflected in the training data. The tree will then beevaluated for new input data and will output a predicted label at theend of the path. FIG. 11B shows a small decision tree with four branchesfor classifying specific flowers based on the sepal length and width ofthe flowers.

One advantage of decision trees is the ease of understanding the model.The predictor space is segmented in a number of simple regions which canbe defined by splitting rules. Splitting rules are the basic element ofdecision trees. One drawback of decision trees, however, is apotentially poor accuracy and a high risk of overfitting the decisiontree to the training data. “Overfitting” may occur when a very detailedtree is created with hundreds of nodes that works perfectly on thetraining data, but has poor results when applied to data not in thetraining set.

One modification of a standard decision tree algorithm is called“Bagging.” This method uses, instead of one decision tree, multipledecision trees. In some cases, hundreds of independent decision treesmay be constructed by using a bootstrap sample of the dataset. Toclassify new input data, the data is processed using all or multiple ofthe trees and a majority vote is taken over the generated predictions.Bagging can be used for regression and classification tasks.

By adding even more randomness to Bagging, a Random Forest algorithm isimplemented. In RF trees, each tree or each node is randomly takingseveral features of input data into consideration. The random featureselection creates independency around the trees compared to regularBagging. Most times the algorithm obtains better results than Bagging,because of better variance and bias tradeoffs. Extremely randomizedtrees take this even further.

In other embodiments, the trained classifier may be advantageouslydefined using a neural network, such as a convolutional neural network(CNN). The inventors have recognized and appreciated that in some cases,a CNN may provide for higher reliability and accuracy than other machinelearning techniques.

Neural networks are implemented as mathematical models that are viewedas something of a metaphor for or simulation of functions of neurons ofan organic brain. Some neurons inside of a brain perform a simple taskequivalent to outputting an electric signal when the input into theneuron exceeds a predetermined threshold. Warren McCulloch and WalterPitts designed the first computational model in 1943 that simulated aneuron with the help of mathematics and a threshold logic for theactivation. The basic structure of a neuron is displayed in the FIG.11C. The inputs x1, x2, . . . , xn are multiplied with weights named w1,w2, . . . , wn. These are then added together with a bias node called b.This value is passed through an activation function. The type offunction chosen depends on the use case and implemented layer. A reasonto add a bias is for example to shift the activation function, in whichcase only higher x values would produce an output. The output can berepresented as a value z with the following formula:

$z = {b + {\sum\limits_{i = 1}^{n}\;{w_{i}x_{i}}}}$

Neural network have multiple layers of connected neurons, whichrepresent complex nonlinear relationships between inputs and outputs. Anexample structure of a neural network is displayed in FIG. 11D. Thearchitecture of a fully connected neural network consists of an inputlayer, hidden layer and output layer. The number of neurons in eachlayer can be adjusted. Feed-forward neural networks are the simplest ofits kind with information moving only in one direction. The movementstarts from the input nodes, goes through the hidden layers and ends inthe output nodes. By implementing multiple hidden layers the NNs aremost common referred to as Deep Neural Networks (DNN). The example ofFIG. 11D is a DNN.

The realization of such complex models is possible through strongcomputation power and a sufficient amount of data. Adjusting theparameters of the network influences the activation logic behind eachneuron. While the network is being trained, the weights of each neuronare adjusted to meet a desired representation of the provided dataset.One training technique, called Gradient Descent (GD), works by finding aminimum of a cost function in an iterative manner. A learning rate isdefined before and specifies the size each step will take to theminimum. This means that a large learning rate might end up causing abouncing optimization while, on the other hand, a very small learningrate might take a long time to arrive at the desired representation.During learning, a change in the cost function is analyzed at each step.Once the cost function is not decreasing (or not decreasingsubstantially) and/or remains on the same level, it is determined thatthe problem has converged and the NN is trained.

Training Neural Networks can result in perfect representations of theprovided training datasets which means bad accuracies for the testdataset. One possibility to prevent Neural Networks from overfitting iscalled dropout. This technique drops a defined number of neurons insideof the network. Dropout can be implemented at the last layer of thenetwork or between every layer for example.

Another way of improving the network is to change activation functions.Each neuron can be influenced by different types of activationfunctions. One known example is the sigmoid function. The function'soutput is always between 0 and 1. The mathematical representation is thefollowing:

${f(t)} = \frac{1}{1 + e^{- t}}$

In other layers it might be more advantageous to gain only values whichare between −1 and 1. This activation function can be realized by thehyperbolic function tanh. It is represented by the following formula:

${\tanh\mspace{14mu}\tanh\mspace{14mu}(x)} = {\frac{2}{1 + e^{{- 2}x}} - 1}$

Saturating functions can result in optimization problems, as thegradient equals 0 at large and small x values. Non-saturated activationfunctions turn out to solve an exploding or vanishing gradient andaccelerate the convergence speed. The Rectified Linear Unit, short ReLU,is another activation function. Its output remains 0 for all negativevalues and does not change for positive values. The ReLU function isrepresented by the following mathematical formula:f(x)=max(0,x)

The early successes to solve new complex tasks cleared the way todevelop new categories of Neural Networks. Compared to early NeuralNetworks, models available in 2018 are far more advanced and cansubstitute more and more work done by humans.

When time-series data is to be input to a Neural Network (such as insome embodiments described herein), this may present complexities inNeural Networks. Training simple NNs on time series data would beinefficient, because the network would adjust its parameters on valuesgiven to particular time steps. Instead it is more efficient for thenetwork to look for patterns in the structure of the data represented.Taking this into consideration, one specific type of Neural Network maybe an advantageous option in some embodiments.

Convolutional Neural Networks (CNNs) are a subclass of Neural Networks.With the amount of collected data in some scenarios (e.g., for imagerecognition), a Neural Network can get very complex and hard to train.CNNs can produce very close accuracies and use less connections as wellas parameters. This makes them very powerful and easier to train.

The main difference between a CNN and a standard NN is that the sumpresented in the neural formula for the NN is substituted by aconvolutional operation. These layers are called convolutional layers.Furthermore, CNNs often have layers that reduce the resolution of thedata, referred to as pooling layers. The architecture of the network isseparated into at least convolutional layers, pooling layers, and anoutput layer.

Convolutional layers determine which inputs are fed into each neuron,potentially with the help of filters applying filter parameters. Thesefilters can be constructed using different sizes, defined by a kernelvariable. The purpose of a filter is to evaluate data and multiply eachfilter parameter with a respective value in the input, which may beconsidered akin to element-by-element multiplication of matrices.

The filter will typically create exactly one output for each portion ofinput data. The manner in which the filter evaluates the input data canbe defined by changing the “stride value.” A stride of 1 would mean thefilter moves one data value at a time. The result of all operationscreates new data having a same data size as the input data. One examplefilter with the dimensions 3×3 is shown in FIG. 11E. The parametersinside the filters are randomly chosen before training the network.

FIG. 11F represents an input data having dimensions of 32×32×3. Theinput data has data dimensions of 32×32×3. The filter is defined withdimensions of 5×5×3, and is displayed in FIG. 11F in the middle of theinput data. The filter convolves over the image with defined steps,called strides, to process each grouping of input data with the filter.Since in this case the filter only moves one step at a time, the strideis equal to one. The output will be of size 28×28 times the number offilters used. In this example, 5 filters are used. Therefore, the outputwould be 28×28×5. In this first layer, each filter would include 76parameters, resulting from the size 5*5*3 dimensions and one biasparameter. This adds up to a total of 380 parameters used across fivefilters. Training the network would mean changing the values of the 380parameters in a way that the network could better differentiate thedifferent labeled data.

The output of each neuron in CNNs may depend also on the type ofactivation function used. The activation function tanh, discussed above,is converges much more slowly during training, because of the saturatingnonlinearity. ReLUs may show faster training. As a result, it may beadvantageous to use ReLUs as the activation function in some or alllayers, or in some embodiments in all layers except the last layer.

Convolutional layers are often followed by pooling layers, whichdecrease the resolution of the data. One example is shown in FIG. 11G,with pooling layer performing a reduction from 2×2 values to only onevalue, to reduce overall data from dimensions of 4×4 to dimensions of3×3. This is done by choosing the maximum value of the 4 valuesidentified by a pooler. Other pooling techniques take the average of allvalues for reduction. This pattern of convolutional layers and afollowed pooling layer may be repeated several times in architectures ofsome embodiments.

The last layer, called output layer, will flatten the data and use eachdata value as an input for a fully-connected neural network layer, whichis a common layer of a NN, as discussed above. A SoftMax activationfunction may be used to make sure that, for a classification problem,the different prediction likelihoods for different classes sum to 1. Themathematical representation of this method is represented by thefollowing formula:

${\sigma(z)}_{j} = \frac{e^{z_{j}}}{\sum\limits_{k = 1}^{m}\; e^{z_{k}}}$

FIG. 11H shows an example of a CNN architecture with two convolutionallayers and two pooling layers.

For training a CNN, one technique called mini-batch stochastic gradientdescent may be used. The method can be described in 4 steps that aresequenced in each iteration:

1) Take a sample of data, defined as batch

2) Forward propagate the input through the network to receive the loss

3) Use backpropagation to calculate the gradient

4) Update the parameters by a defined learning rate using the gradient

This procedure is repeated for all batches. Epochs that were alsodefined before set the amount of training the Neural Network may also beused to successively train the network on the input data, with an epochbeing a complete pass of all the training data through the CNN. Inparticular, in each iteration of an epoch, a number of samples oftraining data equal to a batch size are processed. An epoch may includemultiple iterations, to process all of the training data during theepoch. By training the neural network in multiple epochs, the trainingdata may be input to the neural network multiple times.

In some embodiments operating in accordance with techniques describedherein, a CNN may be trained (e.g., using Gradient Descent or anothertechnique) to identify different classes of vehicle data, associatedwith whether a collision occurred and/or with different characteristicsof collisions (e.g., severity, angle of impact, etc.). In some suchembodiments, a batch size of 150 may be chosen, and the CNN may betrained over 200 epochs. The CNN of some embodiments may include aninput layer followed by four sets of layers, where each set includes twoconvolutional layers followed by a pooling layer that reduces the datadimensions by two and a dropout of 25% to mitigate risk of overfitting,and a fully-connected output layer that flattens and combines alldimensions with a SoftMax function to ensure the likelihoods for the setof classes/predictions sum to 1.

CNNs are mostly used in image classification problems. Such networks areconfigured for use with data (e.g., image pixels) with definedpositions. Transferring this thinking to a time series, the time serieshas also values that happen to a certain point in time. This can createa requirement, though, that the time series be uniformly sampled, tohave fixed positions in time. In some embodiments, input data mayinclude a time series with five dimensions (acceleration in x, y, z;magnitude of total acceleration; and speed) which each have values forevery 10 ms over a time frame of 20 seconds. The CNN may be trained withthis data to find patterns automatically, which can be used to classifyother data.

When implementing the convolution, it is possible to convolve over thetime series with a one-dimensional filter that will create another timeseries. In some embodiments, the filter may be chosen to have a kernelsize of 5. This means that the filter will consider five steps, which isequal to 50 ms over the time series. Since the input data has fivechannels (the acceleration in x, y, z; the magnitude; and speed), thefilter of this example will be of size 5×5. That means every filtercontains 25 parameters, which may be initialized with a random numberand change through the training process. One formula to calculate thenumber of parameters per layer may be the following:total_params_per_layer=(filter_width*channels+1)*number_of_filters

The number one that is added inside of the bracket represents a biasthat is included in every neuron.

The process of convolving over the time series is described in FIG. 11Ifor the first convolutional layer. The filter that starts from the leftwith the size 5×5 is doing an elementwise multiplication and writes theresult into the next time series below. With a padding defined as same,the size of the time series that is created will be equal to theoriginal one. This is done by adding zeros to the end of the time seriesand convolving over these as well. By using 18 filters the resultingoutput will be a time series of 2,000 steps with 18 channels.

The number of filters chosen will determine the number of new channelsfor the input of the next layer. In some embodiments, 18 filters may bechosen for the first layer. This means the second convolutional layerwill take a time series as input which consists of 18 dimensions. Usinganother kernel size of 5 will create a new filter which has dimensions5×18. For this filter 90 parameters are trained for each filter.

As discussed above, in some embodiments the CNN architecture may includefour parts, in which each has a one-dimensional convolutional layerfollowed by another one-dimensional convolutional layer withoutdecreasing the step size and, after these two layers, a pooling layerthat decreases the step size by two and addition of a dropout of 25%that prevents the network from overfitting. These four parts areconnected in sequence and feed into a fully connected layer combines alldimensions and a SoftMax returns three predictions for the three definedclasses.

FIG. 12 illustrates one or more techniques that may be implemented insome embodiments to train a neural network (e.g., a CNN) to implement aclassifier that may be used by a collision detection facility, inaccordance with techniques described herein.

The process 1200 of FIG. 12 may be performed during a configurationphase, in which a collision detection facility is configured prior touse such as in the processes 250 of FIG. 2B and 1000 of FIG. 10 . Theconfiguration may be performed by an administrator, such as a humanadministrator, setting up the collision detection system for thesubsequent use.

The process 1200 of FIG. 12 begins in block 1202, in which datadescribing vehicles moving in normal movements and/or data describingvehicles engaged in collisions is obtained. Data describing normalmovements may be used to train a classifier to recognize data that isassociated with movements that are not collisions, to define one or moreclasses associated with normal driving or otherwise not-collisions. Datadescribing vehicles engaged in collisions may be used to train theclassifier to recognize data that is associated with collisions or withdifferent types of collisions, such as with collisions of differentseverities or collisions with different impact angles. The data that isobtained in block 1202 may include acceleration data or other movementsof movement data described above (magnitude of total acceleration,speed), or other data describing a vehicle (e.g., ECU or OBD data). Thedata that is obtained may be for a time period that matches the timeperiod used by the collision detection facility (e.g., that was definedin block 1004 of FIG. 10 , or block 254 of FIG. 2B).

The data obtained in block 1200 may not be labeled or curated data, andmay not be arranged in a way that a classifier could be clearly trainedon. For example, the data may be mixed together, with data for differentscenarios not labeled or differentiated. To train the classifier, it maybe helpful to at least differentiate and train the data, as there may besome advantages to supervised or semi-supervised training rather thanrelying solely on unsupervised learning.

Accordingly, in block 1202, the data describing vehicles engaged incollisions may be separated out in block 1204, such that data associatedwith different collision scenarios is separated. This process may bemanual or automatic, depending on what data is available as a basis tofor which to conduct a differentiating. Data associated withnot-collisions may not be differentiated in some embodiments, resultingin one class being trained that has features for all different types ofnormal movements. In other embodiments, though, different classes ofnot-collisions may be defined to aid in more reliably identifyingnot-collisions.

Once the data is separated in block 1204, in block 1206 the differentcategories of collisions or other information may be labeled withwhether they reflect a collision or characteristics of the type ofcollision they reflect (e.g., severity, angle of impact). In block 1208,the labeled data may then be separated into clusters by a machinelearning engine and features identified with each cluster identified bythe machine learning engine, to define the clusters and define theclasses. It is these features that will then be used to subsequentlymatch data for a potential collision to a class, by looking for apotential match between the data. To repeat an example from above, astraightforward rear-end collision may include movements that areprimarily forward-backward movements, while a collision in which thevehicle is struck broadside by another vehicle may be associated withprimarily right-left movement data. The trained classifier may, based onthe labeled data from block 1206, draw these same conclusions byobserving that the rear-end collision is associated largely withforward-backward movement data while a broadside collision is largelyassociated with right-left data.

Based on these learned parameters of each of the clusters, a trainedclassifier is created that includes each of the classes defined from theclusters. The trained classifier may then be used by a collisiondetection facility, such as in the manner described above.

While not discussed above in connection with FIG. 12 , it should beappreciated that it would be advantageous if, when movement data isobtained in block 402 that is for a time period, the data would includesamples generated at the same time scale (e.g., same interval), and overthe same period of time, as the data that will be collected by acollision detection facility for a potential collision. Including thedata for the same length of time period and for the same interval mayease classification of the data input for a potential collision, as theinput data will be temporally aligned with the training of theclassifier. In some embodiments, the time period may be 20 seconds andthe sampling rate for the movement data may be 10 ms, meaning there are100 samples per second.

In some cases, the data that is obtained in block 1202 to be used intraining a system may not be aligned in this manner with the data thatwill be collected by a collision detection facility. For example, thedata may be for a shorter or longer time period, or may include samplesgenerated at a different interval. The movement data may therefore beprepared for training in some embodiments, by generating data for thesame time period and at the same interval. If in the input data is for alonger time period, the data may be truncated to match the desired timeperiod length. If the time period is shorter, additional data may begenerated by interpolating the available data. As another example, ifthe input data includes samples at a different interval than the datawill later be collected by a collision detection facility, such as aslower or faster sampling rate, the input data will be sampled and/orinterpolated to generate data at the desired time intervals and for thedesired time period. For example, data points in the input data that areadjacent to a time at which a data point is desired (for a time thataligns with the desired sampling rate) will be interpolated to generatethe desired data point.

In some embodiments, Dynamic Time Warping (DTW) is used to process inputtime series data and prepare it for analysis using the trainedclassifier.

In some embodiments, the magnitudes of input data points may also benormalized based on the scale of input training data, to yield valuesthat are on a similar scale.

In this manner, a classifier can be trained based on collision data togenerate information on whether a collision has occurred and, if so, oneor more characteristics of that collision.

FIGS. 13A-13E illustrate examples of data with which some embodimentsmay operate, as an example of how techniques described herein may beimplemented in some embodiments.

FIGS. 13A-13C illustrate examples of graphs of movement data that may beused to train a classifier or that may be compared to a trainedclassifier to determine whether a collision occurred and/or tocharacterize the collision. The example of FIG. 13A is associated withcollisions in which a vehicle is rear-ended or hits an object (e.g.,another vehicle, or an obstacle) straight on with its front end. Theseare forward-backward collisions. The graph of FIG. 13A includes linesfor accelerations in x (forward-backward), y (right-left), and z(up-down) directions for the vehicle, as well as a magnitude of totalacceleration at a time (“vec”), and a speed v. The x-axis of this graphis a time period for a collision, such as the time period discussedabove in connection with block 1004 of FIG. 10 or 254 of FIG. 2B, withthe time between each point representing the sampling interval. As canbe seen from the graphs of the x, y, and z accelerations, the collisionincluded little y (right-left) or z (up-down) acceleration and insteadlargely consists of negative acceleration (deceleration) in the xdirection (forward-backward). This is because, in a typicalforward-backward collision like a rear-ending, most of the accelerationchange is in the forward-backward direction. The classifier may learnthat pattern and subsequently identify situations in which the change inacceleration is largely in the x direction as forward-backwardcollisions like a rear-ending.

FIG. 13B illustrates an example of a graph in which a vehicle was struckfrom an angle, not forward-backward. The graph of FIG. 13B includes thesame lines for the same variables as the graph of FIG. 13A. The graph ofFIG. 13B demonstrates that in angled collisions, there is change inacceleration in both the x direction (forward-backward) and the ydirection (right-left), though little change in the z direction(up-down).

FIG. 13C illustrates an example of a graph in which a vehicle, as partof a collision, left the road. The graph of FIG. 13C includes the samelines for the same variables as the graphs of FIGS. 13A and 13B. As canbe seen in the graph of FIG. 13C, when the vehicle leaves the road,there may be substantial changes not only in the x and y directions, butalso in the z direction (up-down). In each of these lines, there issubstantial change over time, showing that the vehicle moved quite a bitas part of this accident.

Data for different forms of collisions is illustrated in FIG. 13D,formatted as a scatterplot based on the x and y accelerations with colorof the dot indicating accident type and size of the dot indicatingspeed. A collection of bright blue dots is spread throughout the middleof the graph, cluster in a band around the 0 value on the y-axis butspread throughout the x axis. These are dots associated with differentforward-backward collisions, confirming again that a forward-backwardcollision has little change in the y direction (right-left). The greendots, however, show varying values for change in the y direction(right-left) and are all associated with negative change (deceleration)in the x direction (forward-backward). These are points associated withangled impacts, where there is substantial change in the right-leftdirection and the vehicle slows down substantially in theforward-backward direction.

As discussed above, attempting to detect or characterize a collisionusing only acceleration data from a single point during an accident isunreliable. Using techniques described herein, which obtain longitudinalmovement information for a time period surrounding an event associatedwith a potential collision, or other information obtained for a vehiclefor that time period, may be highly reliable. FIG. 13E shows a chartdemonstrating this high reliability, with low false positions or falsenegatives. In the chart, a “1” category is not a collision, a “2”category is a forward-backward collision, and a “3” category is anangled impact collision. The graph shows the predictions generated by asystem trained using the data of FIG. 13D, compared with the actualscenario. As shown by the chart, the trained classifier will accuratelydetermine, in 98.7% of cases, that no collision occurred when there was,in reality, no collision. The classifier never identifies data relatedto a not-collision as reflecting an angled-impact collision, and only1.3% of the time, incorrectly identifies the not-collision as being aforward-backward collision. Similarly, the trained classifier properlyidentifies, in 95.6% of cases, that a forward-backward collision is aforward-backward collision, with the remaining cases limited tomisidentifying the scenario as a not-collision. Lastly, the trainedclassifier correctly concludes, in 98.2% of cases, that an angled-impactcollision is an angled-impact collision, with the misidentificationsevenly spread, in less than 1% of cases, between not-collisions andforward-backward collisions.

Accordingly, the trained system is highly reliable in determiningwhether a collision occurred and, if so, in characterizing an angle ofimpact of the collision. The collision detection facility of someembodiments described herein can therefore be reliably used to determinewhether a collision has occurred and, if so, characteristics of thecollision, to determine an appropriate response to the collision.

Techniques operating according to the principles described herein may beimplemented in any suitable manner. Included in the discussion above area series of flow charts showing the steps and acts of various processesthat determine whether a collision occurred and/or, if so, tocharacterize a collision. The processing and decision blocks of the flowcharts above represent steps and acts that may be included in algorithmsthat carry out these various processes. Algorithms derived from theseprocesses may be implemented as software integrated with and directingthe operation of one or more single- or multi-purpose processors, may beimplemented as functionally-equivalent circuits such as a Digital SignalProcessing (DSP) circuit or an Application-Specific Integrated Circuit(ASIC), or may be implemented in any other suitable manner. It should beappreciated that the flow charts included herein do not depict thesyntax or operation of any particular circuit or of any particularprogramming language or type of programming language. Rather, the flowcharts illustrate the functional information one skilled in the art mayuse to fabricate circuits or to implement computer software algorithmsto perform the processing of a particular apparatus carrying out thetypes of techniques described herein. It should also be appreciatedthat, unless otherwise indicated herein, the particular sequence ofsteps and/or acts described in each flow chart is merely illustrative ofthe algorithms that may be implemented and can be varied inimplementations and embodiments of the principles described herein.

Accordingly, in some embodiments, the techniques described herein may beembodied in computer-executable instructions implemented as software,including as application software, system software, firmware,middleware, embedded code, or any other suitable type of computer code.Such computer-executable instructions may be written using any of anumber of suitable programming languages and/or programming or scriptingtools, and also may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

When techniques described herein are embodied as computer-executableinstructions, these computer-executable instructions may be implementedin any suitable manner, including as a number of functional facilities,each providing one or more operations to complete execution ofalgorithms operating according to these techniques. A “functionalfacility,” however instantiated, is a structural component of a computersystem that, when integrated with and executed by one or more computers,causes the one or more computers to perform a specific operational role.A functional facility may be a portion of or an entire software element.For example, a functional facility may be implemented as a function of aprocess, or as a discrete process, or as any other suitable unit ofprocessing. If techniques described herein are implemented as multiplefunctional facilities, each functional facility may be implemented inits own way; all need not be implemented the same way. Additionally,these functional facilities may be executed in parallel and/or serially,as appropriate, and may pass information between one another using ashared memory on the computer(s) on which they are executing, using amessage passing protocol, or in any other suitable way.

Generally, functional facilities include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the functional facilities may be combined or distributed as desiredin the systems in which they operate. In some implementations, one ormore functional facilities carrying out techniques herein may togetherform a complete software package. These functional facilities may, inalternative embodiments, be adapted to interact with other, unrelatedfunctional facilities and/or processes, to implement a software programapplication

Some exemplary functional facilities have been described herein forcarrying out one or more tasks. It should be appreciated, though, thatthe functional facilities and division of tasks described is merelyillustrative of the type of functional facilities that may implement theexemplary techniques described herein, and that embodiments are notlimited to being implemented in any specific number, division, or typeof functional facilities. In some implementations, all functionality maybe implemented in a single functional facility. It should also beappreciated that, in some implementations, some of the functionalfacilities described herein may be implemented together with orseparately from others (i.e., as a single unit or separate units), orsome of these functional facilities may not be implemented.

Computer-executable instructions implementing the techniques describedherein (when implemented as one or more functional facilities or in anyother manner) may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), a persistent or non-persistent solid-state memory (e.g.,Flash memory, Magnetic RAM, etc.), or any other suitable storage media.Such a computer-readable medium may be implemented in any suitablemanner, including as computer-readable storage media 1406 of FIG. 14described below (i.e., as a portion of a computing device 1400) or as astand-alone, separate storage medium. As used herein, “computer-readablemedia” (also called “computer-readable storage media”) refers totangible storage media. Tangible storage media are non-transitory andhave at least one physical, structural component. In a“computer-readable medium,” as used herein, at least one physical,structural component has at least one physical property that may bealtered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

In some, but not all, implementations in which the techniques may beembodied as computer-executable instructions, these instructions may beexecuted on one or more suitable computing device(s) operating in anysuitable computer system, including the exemplary computer system ofFIG. 1 , or one or more computing devices (or one or more processors ofone or more computing devices) may be programmed to execute thecomputer-executable instructions. A computing device or processor may beprogrammed to execute instructions when the instructions are stored in amanner accessible to the computing device or processor, such as in adata store (e.g., an on-chip cache or instruction register, acomputer-readable storage medium accessible via a bus, acomputer-readable storage medium accessible via one or more networks andaccessible by the device/processor, etc.). Functional facilitiescomprising these computer-executable instructions may be integrated withand direct the operation of a single multi-purpose programmable digitalcomputing device, a coordinated system of two or more multi-purposecomputing device sharing processing power and jointly carrying out thetechniques described herein, a single computing device or coordinatedsystem of computing devices (co-located or geographically distributed)dedicated to executing the techniques described herein, one or moreField-Programmable Gate Arrays (FPGAs) for carrying out the techniquesdescribed herein, or any other suitable system.

FIG. 14 illustrates one exemplary implementation of a computing devicein the form of a computing device 1400 that may be used in a systemimplementing techniques described in FIGS. 1-13E, although others arepossible. It should be appreciated that FIG. 14 is intended neither tobe a depiction of necessary components for a computing device to operatea collision detection facility in accordance with the principlesdescribed herein, nor a comprehensive depiction.

Computing device 1400 may comprise at least one processor 1402, anetwork adapter 1404, and computer-readable storage media 1406.Computing device 1400 may be, for example, a desktop or laptop personalcomputer, a personal digital assistant (PDA), a smart mobile phone, aserver, or any other suitable computing device. Network adapter 1404 maybe any suitable hardware and/or software to enable the computing device1400 to communicate wired and/or wirelessly with any other suitablecomputing device over any suitable computing network. The computingnetwork may include wireless access points, switches, routers, gateways,and/or other networking equipment as well as any suitable wired and/orwireless communication medium or media for exchanging data between twoor more computers, including the Internet. Computer-readable media 1406may be adapted to store data to be processed and/or instructions to beexecuted by processor 1402. Processor 1402 enables processing of dataand execution of instructions. The data and instructions may be storedon the computer-readable storage media 1406.

The data and instructions stored on computer-readable storage media 1406may comprise computer-executable instructions implementing techniqueswhich operate according to the principles described herein. In theexample of FIG. 14 , computer-readable storage media 1406 storescomputer-executable instructions implementing various facilities andstoring various information as described above. Computer-readablestorage media 1406 may store a collision detection facility 1408, atrained classifier 1410 for the facility 1408 (including definitions ofclasses for the classifier), and data 1412 that includes vehicle data,collision data, driver behavior data, which may be collected for apotential collision and analyzed by the collision detection facility1408 and/or used to train the classifier 1410 for subsequent use inanalyzing data regarding a potential collision.

While not illustrated in FIG. 14 , a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets. As another example, acomputing device may receive input information through speechrecognition or in other audible format.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Various aspects of the embodiments described above may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. Any embodiment, implementation, process,feature, etc. described herein as exemplary should therefore beunderstood to be an illustrative example and should not be understood tobe a preferred or advantageous example unless otherwise indicated.

Having thus described several aspects of at least one embodiment, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe principles described herein. Accordingly, the foregoing descriptionand drawings are by way of example only.

What is claimed is:
 1. A method comprising: in response to obtaininginformation regarding a potential collision between a vehicle and anobject, wherein the obtaining comprises receiving information from atelematics monitor of the vehicle: obtaining, for a time periodextending before and after a time of the potential collision, datadescribing the vehicle during the time period; determining, based atleast in part on the data describing the vehicle during the time period,a likelihood that the potential collision is a non-collision event, by:determining, based at least in part on sensor data from the telematicsmonitor, whether the telematics monitor is operating improperly; and inresponse to determining that the telematics monitor of the vehicle isoperating improperly, determining that the potential collision is not acollision; and in response to determining that the likelihood indicatesthat the potential collision is not a non-collision event: triggeringone or more actions responding to the potential collision.
 2. The methodof claim 1, further comprising: in response to determining that thelikelihood indicates that the potential collision is a non-collisionevent: classifying, using at least one trained classifier, the datadescribing the vehicle into at least one of a plurality of classes, eachclass of the plurality of classes being associated with whether thepotential collision is a collision; and in response to the datadescribing the vehicle being classified into a class indicating that thepotential collision is likely to be a collision, triggering the one ormore actions responding to the potential collision.
 3. The method ofclaim 1, further comprising: detecting the potential collision betweenthe vehicle and the object by identifying that the vehicle hasexperienced at least one acceleration that meets at least one criterion.4. The method of claim 3, wherein identifying that the vehicle hasexperienced at least one acceleration that meets at least one criterioncomprises determining that the at least one acceleration of the vehicleat the time has a value larger than a threshold.
 5. The method of claim1, wherein determining whether the telematics monitor is operatingimproperly comprises: determining a number of triggers the telematicsmonitor has generated during a time length; and determining thetelematics monitor is operating improperly if the number of triggers thetelematics monitor has generated during the time length exceeds athreshold.
 6. A method comprising: in response to obtaining informationregarding a potential collision between a vehicle and an object:obtaining, for a time period extending before and after a time of thepotential collision, data describing the vehicle during the time period;determining, based at least in part on the data describing the vehicleduring the time period, a likelihood that the potential collision is anon-collision event, by: determining at least in part, that a locationof the vehicle at the time of the potential collision matches a roadsurface feature prone to causing acceleration events; and in response todetermining that the likelihood indicates that the potential collisionis not a non-collision event: triggering one or more actions respondingto the potential collision.
 7. The method of claim 6, whereindetermining, based at least in part on the data describing the vehicleduring the time period, the likelihood that the potential collision is anon-collision event further comprises: in response to determining thatthe location of the vehicle at the time of the potential collisionmatches the road surface feature prone to causing acceleration events,determining whether the potential collision is associated with thevehicle traversing the road surface feature by determining whetheracceleration data for the vehicle during the time period matches anacceleration signal that would be produced by the vehicle striking theroad surface feature at the location of the vehicle at the time of thepotential collision.
 8. A method comprising: in response to obtaininginformation regarding a potential collision between a vehicle and anobject: obtaining, for a time period extending before and after a timeof the potential collision, data describing the vehicle during the timeperiod; determining, based at least in part on the data describing thevehicle during the time period, a likelihood that the potentialcollision is a non-collision event; in response to determining that thelikelihood indicates that the potential collision is not a non-collisionevent: triggering one or more actions responding to the potentialcollision; and obtaining additional data describing the vehicle during asecond time period from before and to after the potential collision, andwherein determining, based at least in part on the data describing thevehicle during the time period, the likelihood that the potentialcollision is a non-collision event further comprises: analyzing theadditional data to determine whether a behavior of a driver does notmatch any behavior prone to causing a collision; and in response todetermining that the behavior of the driver does not match any behaviorprone to causing a collision, determining that the potential collisionis a non-collision event.
 9. The method of claim 8, wherein the secondtime period is longer than the time period.
 10. A method comprising: inresponse to obtaining information regarding a first potential collisionbetween a vehicle and an object: obtaining, for a time period extendingbefore and after a time of the first potential collision, datadescribing the vehicle during the time period, by: determining a secondpotential collision within the time period; merging the second potentialcollision to the first potential collision; updating the time period toextend before the time of the first potential collision and after thetime of the second potential collision; and obtaining data describingthe vehicle during the updated time period in place of obtaining dataduring the time period; determining, based at least in part on the datadescribing the vehicle during the time period, a likelihood that thepotential collision is a non-collision event; and in response todetermining that the likelihood indicates that the potential collisionis not a non-collision event: triggering one or more actions respondingto the potential collision.
 11. A method comprising: in response toobtaining information regarding a potential collision between a vehicleand an object: obtaining, for a time period extending before and after atime of the potential collision, data describing the vehicle during thetime period, wherein the data describing the vehicle include an enginefault code of the vehicle; determining, based at least in part on thedata describing the vehicle during the time period, a likelihood thatthe potential collision is a non-collision event; and in response todetermining that the likelihood indicates that the potential collisionis not a non-collision event: triggering one or more actions respondingto the potential collision.
 12. A method comprising: in response toobtaining information regarding a potential collision between a vehicleand an object: obtaining, for a time period extending before and after atime of the potential collision, data describing the vehicle during thetime period; determining, based at least in part on the data describingthe vehicle during the time period, a likelihood that the potentialcollision is a non-collision event, by assessing one or more conditionscomprising: whether the vehicle experienced a pull-over around the timethe potential collision; whether the vehicle experienced a speedreduction around the time of the potential collision; whether GlobalPositioning System (GPS) data for the vehicle around the time of thepotential collision is unreliable; whether an acceleration contextlinked to collision existed around the time of the potential collision;or whether the vehicle experienced a stop around the time of thepotential collision in a travel lane at which vehicles do notcustomarily stop; and in response to determining that the likelihoodindicates that the potential collision is not a non-collision event:triggering one or more actions responding to the potential collision.13. The method of claim 12, wherein determining the likelihood that thepotential collision is a non-collision event comprises: generating acollision score based on the one or more conditions; and determining thepotential collision is likely a non-collision event if the collisionscore is below a threshold.
 14. The method of claim 12, furthercomprising determining whether the vehicle experienced the pull-over atthe time around the potential collision by: determining, at least inpart, whether the vehicle stopped within a threshold time of the time ofthe potential collision and within a threshold distance of a location ofthe vehicle at the time of the potential collision.
 15. The method ofclaim 12, further comprising determining whether the vehicle experiencedthe speed reduction around the time of the potential collision by:determining, at least in part, whether a change of speed of the vehiclearound the time of the potential collision is above a speed threshold.16. The method of claim 12, further comprising determining whether GPSdata for the vehicle around the time of the potential collision isunreliable, by: determining, at least in part, whether a number of GPSpoints within the GPS data for the vehicle during the time period isbelow a number threshold.
 17. The method of claim 12, further comprisingdetermining whether the acceleration context linked to collision existedaround the time of the potential collision by: determining whether anumber of impacts from acceleration data for the vehicle during the timeperiod is within a range; and in response to determining that the numberof impacts from the acceleration data for the vehicle during the timeperiod is within the range, determining a score based at least in parton an acceleration magnitude in a horizontal plane from the accelerationdata for the vehicle during the time period, wherein the score isindicative of a likelihood that an acceleration context linked tocollision existed around the time of the potential collision.
 18. Themethod of claim 17, wherein the range is dependent of whether thevehicle is stationary.
 19. The method of claim 12, further comprisingdetermining whether the vehicle experienced a stop around the time ofthe potential collision in a travel lane at which vehicles do notcustomarily stop, by: determining a location of the stop of the vehiclearound the time of the potential collision; determining a time durationof the stop of the vehicle; determining a type of the travel lane thevehicle was traveling based on the stop location; and determining thatthe vehicle experienced a stop in the travel lane at which vehicles donot customarily stop if it is determined that the time duration of thestop of the vehicle is above a duration threshold, wherein the durationthreshold is dependent of the type of the travel lane.
 20. A methodcomprising: in response to obtaining information regarding a potentialcollision between a vehicle and an object: obtaining, for a time periodextending before and after a time of the potential collision, datadescribing the vehicle during the time period; determining, based atleast in part on the data describing the vehicle during the time period,a likelihood that the potential collision is a non-collision event; andin response to determining that the likelihood indicates that thepotential collision is not a non-collision event: classifying, using atleast one trained classifier, the data describing the vehicle into atleast one class of a plurality of classes, each class of the pluralityof classes being associated with whether the potential collision is acollision; determining whether the potential collision is likely to havebeen an actual collision based at least in part on the at least oneclass identified in the classifying; and triggering one or more actionsresponding to the potential collision.
 21. The method of claim 20,wherein each class of the plurality of classes that is associated withwhether the potential collision is a collision is further associatedwith at least one collision characteristic; and wherein the methodfurther comprises: in response to determining that the potentialcollision is likely to have been an actual collision, characterizing thecollision based at least in part on the at least one collisioncharacteristic associated with one or more of the at least one classidentified in the classifying.
 22. A method comprising: in response toobtaining information regarding a potential collision between a vehicleand an object: obtaining, for a time period extending before and after atime of the potential collision, data describing the vehicle during thetime period; determining, based at least in part on the data describingthe vehicle during the time period, a likelihood that the potentialcollision is a non-collision event by determining whether the datadescribing the vehicle during the time period satisfy one or morecriteria for a non-collision event; and in response to determining thatthe likelihood indicates that the potential collision is not anon-collision event: triggering one or more actions responding to thepotential collision.
 23. The method of claim 22, wherein: the one ormore criteria for a non-collision event comprise a plurality of sets ofcriteria, each set of the plurality of sets corresponding to a differentnon-collision event and comprising at least one criterion; anddetermining whether the data describing the vehicle during the timeperiod satisfy one or more criteria for a non-collision event comprises:determining whether the data describing the vehicle during the timeperiod satisfies the at least one criterion of each set of criteria fromthe plurality of sets of criteria; and in response to determining thatthe data satisfies the at least one criterion for a first set ofcriteria of the plurality of sets of criteria, determining that thepotential collision matches a first non-collision event associated withthe first set of criteria.